mirror of https://github.com/ArduPilot/ardupilot
1665 lines
80 KiB
C++
1665 lines
80 KiB
C++
#include <AP_HAL/AP_HAL.h>
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#include "AP_NavEKF3.h"
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#include "AP_NavEKF3_core.h"
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#include <GCS_MAVLink/GCS.h>
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#include <AP_DAL/AP_DAL.h>
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/********************************************************
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* RESET FUNCTIONS *
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********************************************************/
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// Control reset of yaw and magnetic field states
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void NavEKF3_core::controlMagYawReset()
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{
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// Vehicles that can use a zero sideslip assumption (Planes) are a special case
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// They can use the GPS velocity to recover from bad initial compass data
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// This allows recovery for heading alignment errors due to compass faults
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if (assume_zero_sideslip() && (!finalInflightYawInit || !yawAlignComplete) && inFlight) {
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gpsYawResetRequest = true;
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return;
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} else {
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gpsYawResetRequest = false;
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}
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// Quaternion and delta rotation vector that are re-used for different calculations
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Vector3F deltaRotVecTemp;
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QuaternionF deltaQuatTemp;
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bool flightResetAllowed = false;
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bool initialResetAllowed = false;
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if (!finalInflightYawInit) {
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// Use a quaternion division to calculate the delta quaternion between the rotation at the current and last time
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deltaQuatTemp = stateStruct.quat / prevQuatMagReset;
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prevQuatMagReset = stateStruct.quat;
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// convert the quaternion to a rotation vector and find its length
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deltaQuatTemp.to_axis_angle(deltaRotVecTemp);
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// check if the spin rate is OK - high spin rates can cause angular alignment errors
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bool angRateOK = deltaRotVecTemp.length() < 0.1745f;
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initialResetAllowed = angRateOK && tiltAlignComplete;
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flightResetAllowed = angRateOK && !onGround;
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}
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// reset the limit on the number of magnetic anomaly resets for each takeoff
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if (onGround) {
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magYawAnomallyCount = 0;
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}
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// Check if conditions for a interim or final yaw/mag reset are met
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bool finalResetRequest = false;
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bool interimResetRequest = false;
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if (flightResetAllowed && !assume_zero_sideslip()) {
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// check that we have reached a height where ground magnetic interference effects are insignificant
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// and can perform a final reset of the yaw and field states
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finalResetRequest = (stateStruct.position.z - posDownAtTakeoff) < -EKF3_MAG_FINAL_RESET_ALT;
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// check for increasing height
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bool hgtIncreasing = (posDownAtLastMagReset-stateStruct.position.z) > 0.5f;
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ftype yawInnovIncrease = fabsF(innovYaw) - fabsF(yawInnovAtLastMagReset);
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// check for increasing yaw innovations
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bool yawInnovIncreasing = yawInnovIncrease > 0.25f;
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// check that the yaw innovations haven't been caused by a large change in attitude
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deltaQuatTemp = quatAtLastMagReset / stateStruct.quat;
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deltaQuatTemp.to_axis_angle(deltaRotVecTemp);
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bool largeAngleChange = deltaRotVecTemp.length() > yawInnovIncrease;
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// if yaw innovations and height have increased and we haven't rotated much
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// then we are climbing away from a ground based magnetic anomaly and need to reset
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interimResetRequest = !finalInflightYawInit
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&& !finalResetRequest
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&& (magYawAnomallyCount < MAG_ANOMALY_RESET_MAX)
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&& hgtIncreasing
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&& yawInnovIncreasing
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&& !largeAngleChange;
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}
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// an initial reset is required if we have not yet aligned the yaw angle
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bool initialResetRequest = initialResetAllowed && !yawAlignComplete;
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// a combined yaw angle and magnetic field reset can be initiated by:
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magYawResetRequest = magYawResetRequest || // an external request
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initialResetRequest || // an initial alignment performed by all vehicle types using magnetometer
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interimResetRequest || // an interim alignment required to recover from ground based magnetic anomaly
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finalResetRequest; // the final reset when we have achieved enough height to be in stable magnetic field environment
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// Perform a reset of magnetic field states and reset yaw to corrected magnetic heading
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if (magYawResetRequest && use_compass()) {
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// send initial alignment status to console
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if (!yawAlignComplete) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u MAG%u initial yaw alignment complete",(unsigned)imu_index, (unsigned)magSelectIndex);
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}
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// set yaw from a single mag sample
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setYawFromMag();
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// send in-flight yaw alignment status to console
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if (finalResetRequest) {
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u MAG%u in-flight yaw alignment complete",(unsigned)imu_index, (unsigned)magSelectIndex);
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} else if (interimResetRequest) {
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magYawAnomallyCount++;
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GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "EKF3 IMU%u MAG%u ground mag anomaly, yaw re-aligned",(unsigned)imu_index, (unsigned)magSelectIndex);
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}
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// clear the complete flags if an interim reset has been performed to allow subsequent
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// and final reset to occur
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if (interimResetRequest) {
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finalInflightYawInit = false;
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finalInflightMagInit = false;
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}
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// mag states
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if (!magFieldLearned) {
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resetMagFieldStates();
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}
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}
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if (magStateResetRequest) {
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resetMagFieldStates();
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}
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}
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// this function is used to do a forced re-alignment of the yaw angle to align with the horizontal velocity
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// vector from GPS. It is used to align the yaw angle after launch or takeoff.
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void NavEKF3_core::realignYawGPS(bool emergency_reset)
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{
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// get quaternion from existing filter states and calculate roll, pitch and yaw angles
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Vector3F eulerAngles;
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stateStruct.quat.to_euler(eulerAngles.x, eulerAngles.y, eulerAngles.z);
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if (gpsDataDelayed.vel.xy().length_squared() > sq(GPS_VEL_YAW_ALIGN_MIN_SPD)) {
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// calculate course yaw angle
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ftype velYaw = atan2F(stateStruct.velocity.y,stateStruct.velocity.x);
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// calculate course yaw angle from GPS velocity
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ftype gpsYaw = atan2F(gpsDataDelayed.vel.y,gpsDataDelayed.vel.x);
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// Check the yaw angles for consistency
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ftype yawErr = MAX(fabsF(wrap_PI(gpsYaw - velYaw)),fabsF(wrap_PI(gpsYaw - eulerAngles.z)));
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// If the angles disagree by more than 45 degrees and GPS innovations are large or no previous yaw alignment, we declare the magnetic yaw as bad
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bool badMagYaw = ((yawErr > 0.7854f) && (velTestRatio > 1.0f) && (PV_AidingMode == AID_ABSOLUTE)) || !yawAlignComplete;
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// correct yaw angle using GPS ground course if compass yaw bad
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if (badMagYaw) {
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// attempt to use EKF-GSF estimate if available as it is more robust to GPS glitches
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// by default fly forward vehicles use ground course for initial yaw unless the GSF is explicitly selected as the yaw source
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const bool useGSF = !assume_zero_sideslip() || (frontend->sources.getYawSource() == AP_NavEKF_Source::SourceYaw::GSF);
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if (useGSF && EKFGSF_resetMainFilterYaw(emergency_reset)) {
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return;
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}
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// get yaw variance from GPS speed uncertainty
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const ftype gpsVelAcc = fmaxF(gpsSpdAccuracy, ftype(frontend->_gpsHorizVelNoise));
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const ftype gps_yaw_variance = sq(asinF(constrain_float(gpsVelAcc/gpsDataDelayed.vel.xy().length(), -1.0F, 1.0F)));
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if (gps_yaw_variance < sq(radians(GPS_VEL_YAW_ALIGN_MAX_ANG_ERR))) {
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yawAlignGpsValidCount++;
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} else {
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yawAlignGpsValidCount = 0;
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}
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if (yawAlignGpsValidCount >= GPS_VEL_YAW_ALIGN_COUNT_THRESHOLD) {
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yawAlignGpsValidCount = 0;
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// keep roll and pitch and reset yaw
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rotationOrder order;
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bestRotationOrder(order);
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resetQuatStateYawOnly(gpsYaw, gps_yaw_variance, order);
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// reset the velocity and position states as they will be inaccurate due to bad yaw
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ResetVelocity(resetDataSource::GPS);
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ResetPosition(resetDataSource::GPS);
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// send yaw alignment information to console
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw aligned to GPS velocity",(unsigned)imu_index);
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if (use_compass()) {
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// request a mag field reset which may enable us to use the magnetometer if the previous fault was due to bad initialisation
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magStateResetRequest = true;
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// clear the all sensors failed status so that the magnetometers sensors get a second chance now that we are flying
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allMagSensorsFailed = false;
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}
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}
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}
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} else {
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yawAlignGpsValidCount = 0;
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}
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}
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// align the yaw angle for the quaternion states to the given yaw angle which should be at the fusion horizon
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void NavEKF3_core::alignYawAngle(const yaw_elements &yawAngData)
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{
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// update quaternion states and covariances
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resetQuatStateYawOnly(yawAngData.yawAng, sq(MAX(yawAngData.yawAngErr, 1.0e-2)), yawAngData.order);
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// send yaw alignment information to console
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw aligned",(unsigned)imu_index);
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}
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/********************************************************
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* FUSE MEASURED_DATA *
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********************************************************/
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// select fusion of magnetometer data
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void NavEKF3_core::SelectMagFusion()
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{
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// clear the flag that lets other processes know that the expensive magnetometer fusion operation has been performed on that time step
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// used for load levelling
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magFusePerformed = false;
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// get default yaw source
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const AP_NavEKF_Source::SourceYaw yaw_source = frontend->sources.getYawSource();
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if (yaw_source != yaw_source_last) {
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yaw_source_last = yaw_source;
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yaw_source_reset = true;
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}
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// Store yaw angle when moving for use as a static reference when not moving
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if (!onGroundNotMoving) {
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if (fabsF(prevTnb[0][2]) < fabsF(prevTnb[1][2])) {
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// A 321 rotation order is best conditioned because the X axis is closer to horizontal than the Y axis
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yawAngDataStatic.order = rotationOrder::TAIT_BRYAN_321;
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yawAngDataStatic.yawAng = atan2F(prevTnb[0][1], prevTnb[0][0]);
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} else {
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// A 312 rotation order is best conditioned because the Y axis is closer to horizontal than the X axis
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yawAngDataStatic.order = rotationOrder::TAIT_BRYAN_312;
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yawAngDataStatic.yawAng = atan2F(-prevTnb[1][0], prevTnb[1][1]);
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}
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yawAngDataStatic.yawAngErr = MAX(frontend->_yawNoise, 0.05f);
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yawAngDataStatic.time_ms = imuDataDelayed.time_ms;
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}
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// Handle case where we are not using a yaw sensor of any type and attempt to reset the yaw in
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// flight using the output from the GSF yaw estimator or GPS ground course.
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if ((yaw_source == AP_NavEKF_Source::SourceYaw::GSF) ||
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(!use_compass() &&
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yaw_source != AP_NavEKF_Source::SourceYaw::GPS &&
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yaw_source != AP_NavEKF_Source::SourceYaw::GPS_COMPASS_FALLBACK &&
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yaw_source != AP_NavEKF_Source::SourceYaw::EXTNAV)) {
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if ((!yawAlignComplete || yaw_source_reset) && ((yaw_source != AP_NavEKF_Source::SourceYaw::GSF) || (EKFGSF_yaw_valid_count >= GSF_YAW_VALID_HISTORY_THRESHOLD))) {
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realignYawGPS(false);
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yaw_source_reset = false;
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} else {
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yaw_source_reset = false;
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}
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if (imuSampleTime_ms - lastSynthYawTime_ms > 140) {
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// use the EKF-GSF yaw estimator output as this is more robust than the EKF can achieve without a yaw measurement
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// for non fixed wing platform types
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ftype gsfYaw, gsfYawVariance;
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const bool didUseEKFGSF = yawAlignComplete && (yaw_source == AP_NavEKF_Source::SourceYaw::GSF) && EKFGSF_getYaw(gsfYaw, gsfYawVariance) && !assume_zero_sideslip() && fuseEulerYaw(yawFusionMethod::GSF);
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// fallback methods
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if (!didUseEKFGSF) {
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if (onGroundNotMoving) {
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// fuse last known good yaw angle before we stopped moving to allow yaw bias learning when on ground before flight
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fuseEulerYaw(yawFusionMethod::STATIC);
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} else if (onGround || PV_AidingMode == AID_NONE || (P[0][0]+P[1][1]+P[2][2]+P[3][3] > 0.01f)) {
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// prevent uncontrolled yaw variance growth that can destabilise the covariance matrix
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// by fusing a zero innovation
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fuseEulerYaw(yawFusionMethod::PREDICTED);
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}
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}
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magTestRatio.zero();
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yawTestRatio = 0.0f;
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lastSynthYawTime_ms = imuSampleTime_ms;
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}
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return;
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}
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// Handle case where we are using GPS yaw sensor instead of a magnetomer
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if (yaw_source == AP_NavEKF_Source::SourceYaw::GPS || yaw_source == AP_NavEKF_Source::SourceYaw::GPS_COMPASS_FALLBACK) {
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bool have_fused_gps_yaw = false;
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if (storedYawAng.recall(yawAngDataDelayed,imuDataDelayed.time_ms)) {
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if (tiltAlignComplete && (!yawAlignComplete || yaw_source_reset)) {
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alignYawAngle(yawAngDataDelayed);
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yaw_source_reset = false;
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have_fused_gps_yaw = true;
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lastSynthYawTime_ms = imuSampleTime_ms;
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last_gps_yaw_fuse_ms = imuSampleTime_ms;
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} else if (tiltAlignComplete && yawAlignComplete) {
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have_fused_gps_yaw = fuseEulerYaw(yawFusionMethod::GPS);
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if (have_fused_gps_yaw) {
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last_gps_yaw_fuse_ms = imuSampleTime_ms;
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}
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}
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last_gps_yaw_ms = imuSampleTime_ms;
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} else if (tiltAlignComplete && !yawAlignComplete) {
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// External yaw sources can take significant time to start providing yaw data so
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// wuile waiting, fuse a 'fake' yaw observation at 7Hz to keeop the filter stable
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if (imuSampleTime_ms - lastSynthYawTime_ms > 140) {
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yawAngDataDelayed.yawAngErr = MAX(frontend->_yawNoise, 0.05f);
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// update the yaw angle using the last estimate which will be used as a static yaw reference when movement stops
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if (!onGroundNotMoving) {
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// prevent uncontrolled yaw variance growth by fusing a zero innovation
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fuseEulerYaw(yawFusionMethod::PREDICTED);
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} else {
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// fuse last known good yaw angle before we stopped moving to allow yaw bias learning when on ground before flight
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fuseEulerYaw(yawFusionMethod::STATIC);
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}
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lastSynthYawTime_ms = imuSampleTime_ms;
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}
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} else if (tiltAlignComplete && yawAlignComplete && onGround && imuSampleTime_ms - last_gps_yaw_fuse_ms > 10000) {
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// handle scenario where we were using GPS yaw previously, but the yaw fusion has timed out.
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yaw_source_reset = true;
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}
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if (yaw_source == AP_NavEKF_Source::SourceYaw::GPS) {
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// no fallback
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return;
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}
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// get new mag data into delay buffer
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readMagData();
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if (have_fused_gps_yaw) {
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if (gps_yaw_mag_fallback_active) {
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gps_yaw_mag_fallback_active = false;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw external",(unsigned)imu_index);
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}
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// update mag bias from GPS yaw
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gps_yaw_mag_fallback_ok = learnMagBiasFromGPS();
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return;
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}
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// we don't have GPS yaw data and are configured for
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// fallback. If we've only just lost GPS yaw
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if (imuSampleTime_ms - last_gps_yaw_ms < 10000) {
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// don't fallback to magnetometer fusion for 10s
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return;
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}
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if (!gps_yaw_mag_fallback_ok) {
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// mag was not consistent enough with GPS to use it as
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// fallback
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return;
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}
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if (!inFlight) {
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// don't fall back if not flying but reset to GPS yaw if it becomes available
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return;
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}
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if (!gps_yaw_mag_fallback_active) {
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gps_yaw_mag_fallback_active = true;
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GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw fallback active",(unsigned)imu_index);
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}
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// fall through to magnetometer fusion
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}
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#if EK3_FEATURE_EXTERNAL_NAV
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// Handle case where we are using an external nav for yaw
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const bool extNavYawDataToFuse = storedExtNavYawAng.recall(extNavYawAngDataDelayed, imuDataDelayed.time_ms);
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if (yaw_source == AP_NavEKF_Source::SourceYaw::EXTNAV) {
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if (extNavYawDataToFuse) {
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if (tiltAlignComplete && (!yawAlignComplete || yaw_source_reset)) {
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alignYawAngle(extNavYawAngDataDelayed);
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yaw_source_reset = false;
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} else if (tiltAlignComplete && yawAlignComplete) {
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fuseEulerYaw(yawFusionMethod::EXTNAV);
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}
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last_extnav_yaw_fusion_ms = imuSampleTime_ms;
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} else if (tiltAlignComplete && !yawAlignComplete) {
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// External yaw sources can take significant time to start providing yaw data so
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// while waiting, fuse a 'fake' yaw observation at 7Hz to keep the filter stable
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if (imuSampleTime_ms - lastSynthYawTime_ms > 140) {
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// update the yaw angle using the last estimate which will be used as a static yaw reference when movement stops
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if (!onGroundNotMoving) {
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// prevent uncontrolled yaw variance growth by fusing a zero innovation
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fuseEulerYaw(yawFusionMethod::PREDICTED);
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} else {
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// fuse last known good yaw angle before we stopped moving to allow yaw bias learning when on ground before flight
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fuseEulerYaw(yawFusionMethod::STATIC);
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}
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lastSynthYawTime_ms = imuSampleTime_ms;
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}
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}
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}
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#endif // EK3_FEATURE_EXTERNAL_NAV
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// If we are using the compass and the magnetometer has been unhealthy for too long we declare a timeout
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if (magHealth) {
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magTimeout = false;
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lastHealthyMagTime_ms = imuSampleTime_ms;
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} else if ((imuSampleTime_ms - lastHealthyMagTime_ms) > frontend->magFailTimeLimit_ms && use_compass()) {
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magTimeout = true;
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}
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if (yaw_source != AP_NavEKF_Source::SourceYaw::GPS_COMPASS_FALLBACK) {
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// check for and read new magnetometer measurements. We don't
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// read for GPS_COMPASS_FALLBACK as it has already been read
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// above
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readMagData();
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}
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// check for availability of magnetometer or other yaw data to fuse
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magDataToFuse = storedMag.recall(magDataDelayed,imuDataDelayed.time_ms);
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// Control reset of yaw and magnetic field states if we are using compass data
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if (magDataToFuse) {
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if (yaw_source_reset && (yaw_source == AP_NavEKF_Source::SourceYaw::COMPASS ||
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yaw_source == AP_NavEKF_Source::SourceYaw::GPS_COMPASS_FALLBACK)) {
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magYawResetRequest = true;
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yaw_source_reset = false;
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}
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controlMagYawReset();
|
|
}
|
|
|
|
// determine if conditions are right to start a new fusion cycle
|
|
// wait until the EKF time horizon catches up with the measurement
|
|
bool dataReady = (magDataToFuse && statesInitialised && use_compass() && yawAlignComplete);
|
|
if (dataReady) {
|
|
// use the simple method of declination to maintain heading if we cannot use the magnetic field states
|
|
if(inhibitMagStates || magStateResetRequest || !magStateInitComplete) {
|
|
fuseEulerYaw(yawFusionMethod::MAGNETOMETER);
|
|
|
|
// zero the test ratio output from the inactive 3-axis magnetometer fusion
|
|
magTestRatio.zero();
|
|
|
|
} else {
|
|
// if we are not doing aiding with earth relative observations (eg GPS) then the declination is
|
|
// maintained by fusing declination as a synthesised observation
|
|
// We also fuse declination if we are using the WMM tables
|
|
if (PV_AidingMode != AID_ABSOLUTE ||
|
|
(frontend->_mag_ef_limit > 0 && have_table_earth_field)) {
|
|
FuseDeclination(0.34f);
|
|
}
|
|
// fuse the three magnetometer componenents using sequential fusion for each axis
|
|
FuseMagnetometer();
|
|
// zero the test ratio output from the inactive simple magnetometer yaw fusion
|
|
yawTestRatio = 0.0f;
|
|
}
|
|
}
|
|
|
|
// If the final yaw reset has been performed and the state variances are sufficiently low
|
|
// record that the earth field has been learned.
|
|
if (!magFieldLearned && finalInflightMagInit) {
|
|
magFieldLearned = (P[16][16] < sq(0.01f)) && (P[17][17] < sq(0.01f)) && (P[18][18] < sq(0.01f));
|
|
}
|
|
|
|
// record the last learned field variances
|
|
if (magFieldLearned && !inhibitMagStates) {
|
|
earthMagFieldVar.x = P[16][16];
|
|
earthMagFieldVar.y = P[17][17];
|
|
earthMagFieldVar.z = P[18][18];
|
|
bodyMagFieldVar.x = P[19][19];
|
|
bodyMagFieldVar.y = P[20][20];
|
|
bodyMagFieldVar.z = P[21][21];
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Fuse magnetometer measurements using explicit algebraic equations generated with Matlab symbolic toolbox.
|
|
* The script file used to generate these and other equations in this filter can be found here:
|
|
* https://github.com/PX4/ecl/blob/master/matlab/scripts/Inertial%20Nav%20EKF/GenerateNavFilterEquations.m
|
|
*/
|
|
void NavEKF3_core::FuseMagnetometer()
|
|
{
|
|
// perform sequential fusion of magnetometer measurements.
|
|
// this assumes that the errors in the different components are
|
|
// uncorrelated which is not true, however in the absence of covariance
|
|
// data fit is the only assumption we can make
|
|
// so we might as well take advantage of the computational efficiencies
|
|
// associated with sequential fusion
|
|
// calculate observation jacobians and Kalman gains
|
|
|
|
// create aliases for state to make code easier to read:
|
|
const ftype q0 = stateStruct.quat[0];
|
|
const ftype q1 = stateStruct.quat[1];
|
|
const ftype q2 = stateStruct.quat[2];
|
|
const ftype q3 = stateStruct.quat[3];
|
|
const ftype magN = stateStruct.earth_magfield[0];
|
|
const ftype magE = stateStruct.earth_magfield[1];
|
|
const ftype magD = stateStruct.earth_magfield[2];
|
|
const ftype magXbias = stateStruct.body_magfield[0];
|
|
const ftype magYbias = stateStruct.body_magfield[1];
|
|
const ftype magZbias = stateStruct.body_magfield[2];
|
|
|
|
// rotate predicted earth components into body axes and calculate
|
|
// predicted measurements
|
|
const Matrix3F DCM {
|
|
q0*q0 + q1*q1 - q2*q2 - q3*q3,
|
|
2.0f*(q1*q2 + q0*q3),
|
|
2.0f*(q1*q3-q0*q2),
|
|
2.0f*(q1*q2 - q0*q3),
|
|
q0*q0 - q1*q1 + q2*q2 - q3*q3,
|
|
2.0f*(q2*q3 + q0*q1),
|
|
2.0f*(q1*q3 + q0*q2),
|
|
2.0f*(q2*q3 - q0*q1),
|
|
q0*q0 - q1*q1 - q2*q2 + q3*q3
|
|
};
|
|
|
|
const Vector3F MagPred {
|
|
DCM[0][0]*magN + DCM[0][1]*magE + DCM[0][2]*magD + magXbias,
|
|
DCM[1][0]*magN + DCM[1][1]*magE + DCM[1][2]*magD + magYbias,
|
|
DCM[2][0]*magN + DCM[2][1]*magE + DCM[2][2]*magD + magZbias
|
|
};
|
|
|
|
// calculate the measurement innovation for each axis
|
|
innovMag = MagPred - magDataDelayed.mag;
|
|
|
|
// scale magnetometer observation error with total angular rate to allow for timing errors
|
|
const ftype R_MAG = sq(constrain_ftype(frontend->_magNoise, 0.01f, 0.5f)) + sq(frontend->magVarRateScale*imuDataDelayed.delAng.length() / imuDataDelayed.delAngDT);
|
|
|
|
// calculate common expressions used to calculate observation jacobians an innovation variance for each component
|
|
const Vector9 SH_MAG {
|
|
2.0f*magD*q3 + 2.0f*magE*q2 + 2.0f*magN*q1,
|
|
2.0f*magD*q0 - 2.0f*magE*q1 + 2.0f*magN*q2,
|
|
2.0f*magD*q1 + 2.0f*magE*q0 - 2.0f*magN*q3,
|
|
sq(q3),
|
|
sq(q2),
|
|
sq(q1),
|
|
sq(q0),
|
|
2.0f*magN*q0,
|
|
2.0f*magE*q3
|
|
};
|
|
|
|
// Calculate the innovation variance for each axis
|
|
// X axis
|
|
varInnovMag[0] = (P[19][19] + R_MAG + P[1][19]*SH_MAG[0] - P[2][19]*SH_MAG[1] + P[3][19]*SH_MAG[2] - P[16][19]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + (2.0f*q0*q3 + 2.0f*q1*q2)*(P[19][17] + P[1][17]*SH_MAG[0] - P[2][17]*SH_MAG[1] + P[3][17]*SH_MAG[2] - P[16][17]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][17]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][17]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][17]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (2.0f*q0*q2 - 2.0f*q1*q3)*(P[19][18] + P[1][18]*SH_MAG[0] - P[2][18]*SH_MAG[1] + P[3][18]*SH_MAG[2] - P[16][18]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][18]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][18]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][18]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P[19][0] + P[1][0]*SH_MAG[0] - P[2][0]*SH_MAG[1] + P[3][0]*SH_MAG[2] - P[16][0]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][0]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][0]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][0]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[17][19]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][19]*(2.0f*q0*q2 - 2.0f*q1*q3) + SH_MAG[0]*(P[19][1] + P[1][1]*SH_MAG[0] - P[2][1]*SH_MAG[1] + P[3][1]*SH_MAG[2] - P[16][1]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][1]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][1]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][1]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - SH_MAG[1]*(P[19][2] + P[1][2]*SH_MAG[0] - P[2][2]*SH_MAG[1] + P[3][2]*SH_MAG[2] - P[16][2]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][2]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][2]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][2]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[2]*(P[19][3] + P[1][3]*SH_MAG[0] - P[2][3]*SH_MAG[1] + P[3][3]*SH_MAG[2] - P[16][3]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][3]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][3]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][3]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6])*(P[19][16] + P[1][16]*SH_MAG[0] - P[2][16]*SH_MAG[1] + P[3][16]*SH_MAG[2] - P[16][16]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][16]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][16]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][16]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[0][19]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2));
|
|
if (varInnovMag[0] >= R_MAG) {
|
|
faultStatus.bad_xmag = false;
|
|
} else {
|
|
// the calculation is badly conditioned, so we cannot perform fusion on this step
|
|
// we reset the covariance matrix and try again next measurement
|
|
CovarianceInit();
|
|
faultStatus.bad_xmag = true;
|
|
return;
|
|
}
|
|
|
|
// Y axis
|
|
varInnovMag[1] = (P[20][20] + R_MAG + P[0][20]*SH_MAG[2] + P[1][20]*SH_MAG[1] + P[2][20]*SH_MAG[0] - P[17][20]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - (2.0f*q0*q3 - 2.0f*q1*q2)*(P[20][16] + P[0][16]*SH_MAG[2] + P[1][16]*SH_MAG[1] + P[2][16]*SH_MAG[0] - P[17][16]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][16]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][16]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][16]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (2.0f*q0*q1 + 2.0f*q2*q3)*(P[20][18] + P[0][18]*SH_MAG[2] + P[1][18]*SH_MAG[1] + P[2][18]*SH_MAG[0] - P[17][18]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][18]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][18]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][18]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P[20][3] + P[0][3]*SH_MAG[2] + P[1][3]*SH_MAG[1] + P[2][3]*SH_MAG[0] - P[17][3]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][3]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][3]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][3]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - P[16][20]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][20]*(2.0f*q0*q1 + 2.0f*q2*q3) + SH_MAG[2]*(P[20][0] + P[0][0]*SH_MAG[2] + P[1][0]*SH_MAG[1] + P[2][0]*SH_MAG[0] - P[17][0]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][0]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][0]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][0]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[1]*(P[20][1] + P[0][1]*SH_MAG[2] + P[1][1]*SH_MAG[1] + P[2][1]*SH_MAG[0] - P[17][1]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][1]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][1]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][1]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[0]*(P[20][2] + P[0][2]*SH_MAG[2] + P[1][2]*SH_MAG[1] + P[2][2]*SH_MAG[0] - P[17][2]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][2]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][2]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][2]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6])*(P[20][17] + P[0][17]*SH_MAG[2] + P[1][17]*SH_MAG[1] + P[2][17]*SH_MAG[0] - P[17][17]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][17]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][17]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][17]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - P[3][20]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2));
|
|
if (varInnovMag[1] >= R_MAG) {
|
|
faultStatus.bad_ymag = false;
|
|
} else {
|
|
// the calculation is badly conditioned, so we cannot perform fusion on this step
|
|
// we reset the covariance matrix and try again next measurement
|
|
CovarianceInit();
|
|
faultStatus.bad_ymag = true;
|
|
return;
|
|
}
|
|
|
|
// Z axis
|
|
varInnovMag[2] = (P[21][21] + R_MAG + P[0][21]*SH_MAG[1] - P[1][21]*SH_MAG[2] + P[3][21]*SH_MAG[0] + P[18][21]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + (2.0f*q0*q2 + 2.0f*q1*q3)*(P[21][16] + P[0][16]*SH_MAG[1] - P[1][16]*SH_MAG[2] + P[3][16]*SH_MAG[0] + P[18][16]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][16]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][16]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][16]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (2.0f*q0*q1 - 2.0f*q2*q3)*(P[21][17] + P[0][17]*SH_MAG[1] - P[1][17]*SH_MAG[2] + P[3][17]*SH_MAG[0] + P[18][17]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][17]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][17]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][17]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P[21][2] + P[0][2]*SH_MAG[1] - P[1][2]*SH_MAG[2] + P[3][2]*SH_MAG[0] + P[18][2]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][2]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][2]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][2]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[16][21]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][21]*(2.0f*q0*q1 - 2.0f*q2*q3) + SH_MAG[1]*(P[21][0] + P[0][0]*SH_MAG[1] - P[1][0]*SH_MAG[2] + P[3][0]*SH_MAG[0] + P[18][0]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][0]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][0]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][0]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - SH_MAG[2]*(P[21][1] + P[0][1]*SH_MAG[1] - P[1][1]*SH_MAG[2] + P[3][1]*SH_MAG[0] + P[18][1]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][1]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][1]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][1]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[0]*(P[21][3] + P[0][3]*SH_MAG[1] - P[1][3]*SH_MAG[2] + P[3][3]*SH_MAG[0] + P[18][3]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][3]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][3]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][3]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6])*(P[21][18] + P[0][18]*SH_MAG[1] - P[1][18]*SH_MAG[2] + P[3][18]*SH_MAG[0] + P[18][18]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][18]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][18]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][18]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[2][21]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2));
|
|
if (varInnovMag[2] >= R_MAG) {
|
|
faultStatus.bad_zmag = false;
|
|
} else {
|
|
// the calculation is badly conditioned, so we cannot perform fusion on this step
|
|
// we reset the covariance matrix and try again next measurement
|
|
CovarianceInit();
|
|
faultStatus.bad_zmag = true;
|
|
return;
|
|
}
|
|
|
|
// calculate the innovation test ratios
|
|
for (uint8_t i = 0; i<=2; i++) {
|
|
magTestRatio[i] = sq(innovMag[i]) / (sq(MAX(0.01f * (ftype)frontend->_magInnovGate, 1.0f)) * varInnovMag[i]);
|
|
}
|
|
|
|
// check the last values from all components and set magnetometer health accordingly
|
|
magHealth = (magTestRatio[0] < 1.0f && magTestRatio[1] < 1.0f && magTestRatio[2] < 1.0f);
|
|
|
|
// if the magnetometer is unhealthy, do not proceed further
|
|
if (!magHealth) {
|
|
return;
|
|
}
|
|
|
|
Vector24 H_MAG;
|
|
for (uint8_t obsIndex = 0; obsIndex <= 2; obsIndex++) {
|
|
|
|
if (obsIndex == 0) {
|
|
|
|
for (uint8_t i = 0; i<=stateIndexLim; i++) H_MAG[i] = 0.0f;
|
|
H_MAG[0] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
|
|
H_MAG[1] = SH_MAG[0];
|
|
H_MAG[2] = -SH_MAG[1];
|
|
H_MAG[3] = SH_MAG[2];
|
|
H_MAG[16] = SH_MAG[5] - SH_MAG[4] - SH_MAG[3] + SH_MAG[6];
|
|
H_MAG[17] = 2.0f*q0*q3 + 2.0f*q1*q2;
|
|
H_MAG[18] = 2.0f*q1*q3 - 2.0f*q0*q2;
|
|
H_MAG[19] = 1.0f;
|
|
H_MAG[20] = 0.0f;
|
|
H_MAG[21] = 0.0f;
|
|
|
|
// calculate Kalman gain
|
|
const Vector5 SK_MX {
|
|
1.0f / varInnovMag[0],
|
|
SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6],
|
|
SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2,
|
|
2.0f*q0*q2 - 2.0f*q1*q3,
|
|
2.0f*q0*q3 + 2.0f*q1*q2
|
|
};
|
|
|
|
Kfusion[0] = SK_MX[0]*(P[0][19] + P[0][1]*SH_MAG[0] - P[0][2]*SH_MAG[1] + P[0][3]*SH_MAG[2] + P[0][0]*SK_MX[2] - P[0][16]*SK_MX[1] + P[0][17]*SK_MX[4] - P[0][18]*SK_MX[3]);
|
|
Kfusion[1] = SK_MX[0]*(P[1][19] + P[1][1]*SH_MAG[0] - P[1][2]*SH_MAG[1] + P[1][3]*SH_MAG[2] + P[1][0]*SK_MX[2] - P[1][16]*SK_MX[1] + P[1][17]*SK_MX[4] - P[1][18]*SK_MX[3]);
|
|
Kfusion[2] = SK_MX[0]*(P[2][19] + P[2][1]*SH_MAG[0] - P[2][2]*SH_MAG[1] + P[2][3]*SH_MAG[2] + P[2][0]*SK_MX[2] - P[2][16]*SK_MX[1] + P[2][17]*SK_MX[4] - P[2][18]*SK_MX[3]);
|
|
Kfusion[3] = SK_MX[0]*(P[3][19] + P[3][1]*SH_MAG[0] - P[3][2]*SH_MAG[1] + P[3][3]*SH_MAG[2] + P[3][0]*SK_MX[2] - P[3][16]*SK_MX[1] + P[3][17]*SK_MX[4] - P[3][18]*SK_MX[3]);
|
|
Kfusion[4] = SK_MX[0]*(P[4][19] + P[4][1]*SH_MAG[0] - P[4][2]*SH_MAG[1] + P[4][3]*SH_MAG[2] + P[4][0]*SK_MX[2] - P[4][16]*SK_MX[1] + P[4][17]*SK_MX[4] - P[4][18]*SK_MX[3]);
|
|
Kfusion[5] = SK_MX[0]*(P[5][19] + P[5][1]*SH_MAG[0] - P[5][2]*SH_MAG[1] + P[5][3]*SH_MAG[2] + P[5][0]*SK_MX[2] - P[5][16]*SK_MX[1] + P[5][17]*SK_MX[4] - P[5][18]*SK_MX[3]);
|
|
Kfusion[6] = SK_MX[0]*(P[6][19] + P[6][1]*SH_MAG[0] - P[6][2]*SH_MAG[1] + P[6][3]*SH_MAG[2] + P[6][0]*SK_MX[2] - P[6][16]*SK_MX[1] + P[6][17]*SK_MX[4] - P[6][18]*SK_MX[3]);
|
|
Kfusion[7] = SK_MX[0]*(P[7][19] + P[7][1]*SH_MAG[0] - P[7][2]*SH_MAG[1] + P[7][3]*SH_MAG[2] + P[7][0]*SK_MX[2] - P[7][16]*SK_MX[1] + P[7][17]*SK_MX[4] - P[7][18]*SK_MX[3]);
|
|
Kfusion[8] = SK_MX[0]*(P[8][19] + P[8][1]*SH_MAG[0] - P[8][2]*SH_MAG[1] + P[8][3]*SH_MAG[2] + P[8][0]*SK_MX[2] - P[8][16]*SK_MX[1] + P[8][17]*SK_MX[4] - P[8][18]*SK_MX[3]);
|
|
Kfusion[9] = SK_MX[0]*(P[9][19] + P[9][1]*SH_MAG[0] - P[9][2]*SH_MAG[1] + P[9][3]*SH_MAG[2] + P[9][0]*SK_MX[2] - P[9][16]*SK_MX[1] + P[9][17]*SK_MX[4] - P[9][18]*SK_MX[3]);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = SK_MX[0]*(P[10][19] + P[10][1]*SH_MAG[0] - P[10][2]*SH_MAG[1] + P[10][3]*SH_MAG[2] + P[10][0]*SK_MX[2] - P[10][16]*SK_MX[1] + P[10][17]*SK_MX[4] - P[10][18]*SK_MX[3]);
|
|
Kfusion[11] = SK_MX[0]*(P[11][19] + P[11][1]*SH_MAG[0] - P[11][2]*SH_MAG[1] + P[11][3]*SH_MAG[2] + P[11][0]*SK_MX[2] - P[11][16]*SK_MX[1] + P[11][17]*SK_MX[4] - P[11][18]*SK_MX[3]);
|
|
Kfusion[12] = SK_MX[0]*(P[12][19] + P[12][1]*SH_MAG[0] - P[12][2]*SH_MAG[1] + P[12][3]*SH_MAG[2] + P[12][0]*SK_MX[2] - P[12][16]*SK_MX[1] + P[12][17]*SK_MX[4] - P[12][18]*SK_MX[3]);
|
|
} else {
|
|
// zero indexes 10 to 12
|
|
zero_range(&Kfusion[0], 10, 12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
for (uint8_t index = 0; index < 3; index++) {
|
|
const uint8_t stateIndex = index + 13;
|
|
if (!dvelBiasAxisInhibit[index]) {
|
|
Kfusion[stateIndex] = SK_MX[0]*(P[stateIndex][19] + P[stateIndex][1]*SH_MAG[0] - P[stateIndex][2]*SH_MAG[1] + P[stateIndex][3]*SH_MAG[2] + P[stateIndex][0]*SK_MX[2] - P[stateIndex][16]*SK_MX[1] + P[stateIndex][17]*SK_MX[4] - P[stateIndex][18]*SK_MX[3]);
|
|
} else {
|
|
Kfusion[stateIndex] = 0.0f;
|
|
}
|
|
}
|
|
} else {
|
|
// zero indexes 13 to 15
|
|
zero_range(&Kfusion[0], 13, 15);
|
|
}
|
|
// zero Kalman gains to inhibit magnetic field state estimation
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = SK_MX[0]*(P[16][19] + P[16][1]*SH_MAG[0] - P[16][2]*SH_MAG[1] + P[16][3]*SH_MAG[2] + P[16][0]*SK_MX[2] - P[16][16]*SK_MX[1] + P[16][17]*SK_MX[4] - P[16][18]*SK_MX[3]);
|
|
Kfusion[17] = SK_MX[0]*(P[17][19] + P[17][1]*SH_MAG[0] - P[17][2]*SH_MAG[1] + P[17][3]*SH_MAG[2] + P[17][0]*SK_MX[2] - P[17][16]*SK_MX[1] + P[17][17]*SK_MX[4] - P[17][18]*SK_MX[3]);
|
|
Kfusion[18] = SK_MX[0]*(P[18][19] + P[18][1]*SH_MAG[0] - P[18][2]*SH_MAG[1] + P[18][3]*SH_MAG[2] + P[18][0]*SK_MX[2] - P[18][16]*SK_MX[1] + P[18][17]*SK_MX[4] - P[18][18]*SK_MX[3]);
|
|
Kfusion[19] = SK_MX[0]*(P[19][19] + P[19][1]*SH_MAG[0] - P[19][2]*SH_MAG[1] + P[19][3]*SH_MAG[2] + P[19][0]*SK_MX[2] - P[19][16]*SK_MX[1] + P[19][17]*SK_MX[4] - P[19][18]*SK_MX[3]);
|
|
Kfusion[20] = SK_MX[0]*(P[20][19] + P[20][1]*SH_MAG[0] - P[20][2]*SH_MAG[1] + P[20][3]*SH_MAG[2] + P[20][0]*SK_MX[2] - P[20][16]*SK_MX[1] + P[20][17]*SK_MX[4] - P[20][18]*SK_MX[3]);
|
|
Kfusion[21] = SK_MX[0]*(P[21][19] + P[21][1]*SH_MAG[0] - P[21][2]*SH_MAG[1] + P[21][3]*SH_MAG[2] + P[21][0]*SK_MX[2] - P[21][16]*SK_MX[1] + P[21][17]*SK_MX[4] - P[21][18]*SK_MX[3]);
|
|
} else {
|
|
// zero indexes 16 to 21
|
|
zero_range(&Kfusion[0], 16, 21);
|
|
}
|
|
|
|
// zero Kalman gains to inhibit wind state estimation
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = SK_MX[0]*(P[22][19] + P[22][1]*SH_MAG[0] - P[22][2]*SH_MAG[1] + P[22][3]*SH_MAG[2] + P[22][0]*SK_MX[2] - P[22][16]*SK_MX[1] + P[22][17]*SK_MX[4] - P[22][18]*SK_MX[3]);
|
|
Kfusion[23] = SK_MX[0]*(P[23][19] + P[23][1]*SH_MAG[0] - P[23][2]*SH_MAG[1] + P[23][3]*SH_MAG[2] + P[23][0]*SK_MX[2] - P[23][16]*SK_MX[1] + P[23][17]*SK_MX[4] - P[23][18]*SK_MX[3]);
|
|
} else {
|
|
// zero indexes 22 to 23 = 2
|
|
zero_range(&Kfusion[0], 22, 23);
|
|
}
|
|
|
|
// set flags to indicate to other processes that fusion has been performed and is required on the next frame
|
|
// this can be used by other fusion processes to avoid fusing on the same frame as this expensive step
|
|
magFusePerformed = true;
|
|
} else if (obsIndex == 1) { // Fuse Y axis
|
|
|
|
// calculate observation jacobians
|
|
for (uint8_t i = 0; i<=stateIndexLim; i++) H_MAG[i] = 0.0f;
|
|
H_MAG[0] = SH_MAG[2];
|
|
H_MAG[1] = SH_MAG[1];
|
|
H_MAG[2] = SH_MAG[0];
|
|
H_MAG[3] = 2.0f*magD*q2 - SH_MAG[8] - SH_MAG[7];
|
|
H_MAG[16] = 2.0f*q1*q2 - 2.0f*q0*q3;
|
|
H_MAG[17] = SH_MAG[4] - SH_MAG[3] - SH_MAG[5] + SH_MAG[6];
|
|
H_MAG[18] = 2.0f*q0*q1 + 2.0f*q2*q3;
|
|
H_MAG[19] = 0.0f;
|
|
H_MAG[20] = 1.0f;
|
|
H_MAG[21] = 0.0f;
|
|
|
|
// calculate Kalman gain
|
|
const Vector5 SK_MY {
|
|
1.0f / varInnovMag[1],
|
|
SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6],
|
|
SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2,
|
|
2.0f*q0*q3 - 2.0f*q1*q2,
|
|
2.0f*q0*q1 + 2.0f*q2*q3
|
|
};
|
|
|
|
Kfusion[0] = SK_MY[0]*(P[0][20] + P[0][0]*SH_MAG[2] + P[0][1]*SH_MAG[1] + P[0][2]*SH_MAG[0] - P[0][3]*SK_MY[2] - P[0][17]*SK_MY[1] - P[0][16]*SK_MY[3] + P[0][18]*SK_MY[4]);
|
|
Kfusion[1] = SK_MY[0]*(P[1][20] + P[1][0]*SH_MAG[2] + P[1][1]*SH_MAG[1] + P[1][2]*SH_MAG[0] - P[1][3]*SK_MY[2] - P[1][17]*SK_MY[1] - P[1][16]*SK_MY[3] + P[1][18]*SK_MY[4]);
|
|
Kfusion[2] = SK_MY[0]*(P[2][20] + P[2][0]*SH_MAG[2] + P[2][1]*SH_MAG[1] + P[2][2]*SH_MAG[0] - P[2][3]*SK_MY[2] - P[2][17]*SK_MY[1] - P[2][16]*SK_MY[3] + P[2][18]*SK_MY[4]);
|
|
Kfusion[3] = SK_MY[0]*(P[3][20] + P[3][0]*SH_MAG[2] + P[3][1]*SH_MAG[1] + P[3][2]*SH_MAG[0] - P[3][3]*SK_MY[2] - P[3][17]*SK_MY[1] - P[3][16]*SK_MY[3] + P[3][18]*SK_MY[4]);
|
|
Kfusion[4] = SK_MY[0]*(P[4][20] + P[4][0]*SH_MAG[2] + P[4][1]*SH_MAG[1] + P[4][2]*SH_MAG[0] - P[4][3]*SK_MY[2] - P[4][17]*SK_MY[1] - P[4][16]*SK_MY[3] + P[4][18]*SK_MY[4]);
|
|
Kfusion[5] = SK_MY[0]*(P[5][20] + P[5][0]*SH_MAG[2] + P[5][1]*SH_MAG[1] + P[5][2]*SH_MAG[0] - P[5][3]*SK_MY[2] - P[5][17]*SK_MY[1] - P[5][16]*SK_MY[3] + P[5][18]*SK_MY[4]);
|
|
Kfusion[6] = SK_MY[0]*(P[6][20] + P[6][0]*SH_MAG[2] + P[6][1]*SH_MAG[1] + P[6][2]*SH_MAG[0] - P[6][3]*SK_MY[2] - P[6][17]*SK_MY[1] - P[6][16]*SK_MY[3] + P[6][18]*SK_MY[4]);
|
|
Kfusion[7] = SK_MY[0]*(P[7][20] + P[7][0]*SH_MAG[2] + P[7][1]*SH_MAG[1] + P[7][2]*SH_MAG[0] - P[7][3]*SK_MY[2] - P[7][17]*SK_MY[1] - P[7][16]*SK_MY[3] + P[7][18]*SK_MY[4]);
|
|
Kfusion[8] = SK_MY[0]*(P[8][20] + P[8][0]*SH_MAG[2] + P[8][1]*SH_MAG[1] + P[8][2]*SH_MAG[0] - P[8][3]*SK_MY[2] - P[8][17]*SK_MY[1] - P[8][16]*SK_MY[3] + P[8][18]*SK_MY[4]);
|
|
Kfusion[9] = SK_MY[0]*(P[9][20] + P[9][0]*SH_MAG[2] + P[9][1]*SH_MAG[1] + P[9][2]*SH_MAG[0] - P[9][3]*SK_MY[2] - P[9][17]*SK_MY[1] - P[9][16]*SK_MY[3] + P[9][18]*SK_MY[4]);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = SK_MY[0]*(P[10][20] + P[10][0]*SH_MAG[2] + P[10][1]*SH_MAG[1] + P[10][2]*SH_MAG[0] - P[10][3]*SK_MY[2] - P[10][17]*SK_MY[1] - P[10][16]*SK_MY[3] + P[10][18]*SK_MY[4]);
|
|
Kfusion[11] = SK_MY[0]*(P[11][20] + P[11][0]*SH_MAG[2] + P[11][1]*SH_MAG[1] + P[11][2]*SH_MAG[0] - P[11][3]*SK_MY[2] - P[11][17]*SK_MY[1] - P[11][16]*SK_MY[3] + P[11][18]*SK_MY[4]);
|
|
Kfusion[12] = SK_MY[0]*(P[12][20] + P[12][0]*SH_MAG[2] + P[12][1]*SH_MAG[1] + P[12][2]*SH_MAG[0] - P[12][3]*SK_MY[2] - P[12][17]*SK_MY[1] - P[12][16]*SK_MY[3] + P[12][18]*SK_MY[4]);
|
|
} else {
|
|
// zero indexes 10 to 12
|
|
zero_range(&Kfusion[0], 10, 12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
for (uint8_t index = 0; index < 3; index++) {
|
|
const uint8_t stateIndex = index + 13;
|
|
if (!dvelBiasAxisInhibit[index]) {
|
|
Kfusion[stateIndex] = SK_MY[0]*(P[stateIndex][20] + P[stateIndex][0]*SH_MAG[2] + P[stateIndex][1]*SH_MAG[1] + P[stateIndex][2]*SH_MAG[0] - P[stateIndex][3]*SK_MY[2] - P[stateIndex][17]*SK_MY[1] - P[stateIndex][16]*SK_MY[3] + P[stateIndex][18]*SK_MY[4]);
|
|
} else {
|
|
Kfusion[stateIndex] = 0.0f;
|
|
}
|
|
}
|
|
} else {
|
|
// zero indexes 13 to 15
|
|
zero_range(&Kfusion[0], 13, 15);
|
|
}
|
|
|
|
// zero Kalman gains to inhibit magnetic field state estimation
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = SK_MY[0]*(P[16][20] + P[16][0]*SH_MAG[2] + P[16][1]*SH_MAG[1] + P[16][2]*SH_MAG[0] - P[16][3]*SK_MY[2] - P[16][17]*SK_MY[1] - P[16][16]*SK_MY[3] + P[16][18]*SK_MY[4]);
|
|
Kfusion[17] = SK_MY[0]*(P[17][20] + P[17][0]*SH_MAG[2] + P[17][1]*SH_MAG[1] + P[17][2]*SH_MAG[0] - P[17][3]*SK_MY[2] - P[17][17]*SK_MY[1] - P[17][16]*SK_MY[3] + P[17][18]*SK_MY[4]);
|
|
Kfusion[18] = SK_MY[0]*(P[18][20] + P[18][0]*SH_MAG[2] + P[18][1]*SH_MAG[1] + P[18][2]*SH_MAG[0] - P[18][3]*SK_MY[2] - P[18][17]*SK_MY[1] - P[18][16]*SK_MY[3] + P[18][18]*SK_MY[4]);
|
|
Kfusion[19] = SK_MY[0]*(P[19][20] + P[19][0]*SH_MAG[2] + P[19][1]*SH_MAG[1] + P[19][2]*SH_MAG[0] - P[19][3]*SK_MY[2] - P[19][17]*SK_MY[1] - P[19][16]*SK_MY[3] + P[19][18]*SK_MY[4]);
|
|
Kfusion[20] = SK_MY[0]*(P[20][20] + P[20][0]*SH_MAG[2] + P[20][1]*SH_MAG[1] + P[20][2]*SH_MAG[0] - P[20][3]*SK_MY[2] - P[20][17]*SK_MY[1] - P[20][16]*SK_MY[3] + P[20][18]*SK_MY[4]);
|
|
Kfusion[21] = SK_MY[0]*(P[21][20] + P[21][0]*SH_MAG[2] + P[21][1]*SH_MAG[1] + P[21][2]*SH_MAG[0] - P[21][3]*SK_MY[2] - P[21][17]*SK_MY[1] - P[21][16]*SK_MY[3] + P[21][18]*SK_MY[4]);
|
|
} else {
|
|
// zero indexes 16 to 21
|
|
zero_range(&Kfusion[0], 16, 21);
|
|
}
|
|
|
|
// zero Kalman gains to inhibit wind state estimation
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = SK_MY[0]*(P[22][20] + P[22][0]*SH_MAG[2] + P[22][1]*SH_MAG[1] + P[22][2]*SH_MAG[0] - P[22][3]*SK_MY[2] - P[22][17]*SK_MY[1] - P[22][16]*SK_MY[3] + P[22][18]*SK_MY[4]);
|
|
Kfusion[23] = SK_MY[0]*(P[23][20] + P[23][0]*SH_MAG[2] + P[23][1]*SH_MAG[1] + P[23][2]*SH_MAG[0] - P[23][3]*SK_MY[2] - P[23][17]*SK_MY[1] - P[23][16]*SK_MY[3] + P[23][18]*SK_MY[4]);
|
|
} else {
|
|
// zero indexes 22 to 23
|
|
zero_range(&Kfusion[0], 22, 23);
|
|
}
|
|
|
|
// set flags to indicate to other processes that fusion has been performed and is required on the next frame
|
|
// this can be used by other fusion processes to avoid fusing on the same frame as this expensive step
|
|
magFusePerformed = true;
|
|
}
|
|
else if (obsIndex == 2) // we are now fusing the Z measurement
|
|
{
|
|
// calculate observation jacobians
|
|
for (uint8_t i = 0; i<=stateIndexLim; i++) H_MAG[i] = 0.0f;
|
|
H_MAG[0] = SH_MAG[1];
|
|
H_MAG[1] = -SH_MAG[2];
|
|
H_MAG[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
|
|
H_MAG[3] = SH_MAG[0];
|
|
H_MAG[16] = 2.0f*q0*q2 + 2.0f*q1*q3;
|
|
H_MAG[17] = 2.0f*q2*q3 - 2.0f*q0*q1;
|
|
H_MAG[18] = SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6];
|
|
H_MAG[19] = 0.0f;
|
|
H_MAG[20] = 0.0f;
|
|
H_MAG[21] = 1.0f;
|
|
|
|
// calculate Kalman gain
|
|
const Vector5 SK_MZ {
|
|
1.0f / varInnovMag[2],
|
|
SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6],
|
|
SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2,
|
|
2.0f*q0*q1 - 2.0f*q2*q3,
|
|
2.0f*q0*q2 + 2.0f*q1*q3
|
|
};
|
|
|
|
Kfusion[0] = SK_MZ[0]*(P[0][21] + P[0][0]*SH_MAG[1] - P[0][1]*SH_MAG[2] + P[0][3]*SH_MAG[0] + P[0][2]*SK_MZ[2] + P[0][18]*SK_MZ[1] + P[0][16]*SK_MZ[4] - P[0][17]*SK_MZ[3]);
|
|
Kfusion[1] = SK_MZ[0]*(P[1][21] + P[1][0]*SH_MAG[1] - P[1][1]*SH_MAG[2] + P[1][3]*SH_MAG[0] + P[1][2]*SK_MZ[2] + P[1][18]*SK_MZ[1] + P[1][16]*SK_MZ[4] - P[1][17]*SK_MZ[3]);
|
|
Kfusion[2] = SK_MZ[0]*(P[2][21] + P[2][0]*SH_MAG[1] - P[2][1]*SH_MAG[2] + P[2][3]*SH_MAG[0] + P[2][2]*SK_MZ[2] + P[2][18]*SK_MZ[1] + P[2][16]*SK_MZ[4] - P[2][17]*SK_MZ[3]);
|
|
Kfusion[3] = SK_MZ[0]*(P[3][21] + P[3][0]*SH_MAG[1] - P[3][1]*SH_MAG[2] + P[3][3]*SH_MAG[0] + P[3][2]*SK_MZ[2] + P[3][18]*SK_MZ[1] + P[3][16]*SK_MZ[4] - P[3][17]*SK_MZ[3]);
|
|
Kfusion[4] = SK_MZ[0]*(P[4][21] + P[4][0]*SH_MAG[1] - P[4][1]*SH_MAG[2] + P[4][3]*SH_MAG[0] + P[4][2]*SK_MZ[2] + P[4][18]*SK_MZ[1] + P[4][16]*SK_MZ[4] - P[4][17]*SK_MZ[3]);
|
|
Kfusion[5] = SK_MZ[0]*(P[5][21] + P[5][0]*SH_MAG[1] - P[5][1]*SH_MAG[2] + P[5][3]*SH_MAG[0] + P[5][2]*SK_MZ[2] + P[5][18]*SK_MZ[1] + P[5][16]*SK_MZ[4] - P[5][17]*SK_MZ[3]);
|
|
Kfusion[6] = SK_MZ[0]*(P[6][21] + P[6][0]*SH_MAG[1] - P[6][1]*SH_MAG[2] + P[6][3]*SH_MAG[0] + P[6][2]*SK_MZ[2] + P[6][18]*SK_MZ[1] + P[6][16]*SK_MZ[4] - P[6][17]*SK_MZ[3]);
|
|
Kfusion[7] = SK_MZ[0]*(P[7][21] + P[7][0]*SH_MAG[1] - P[7][1]*SH_MAG[2] + P[7][3]*SH_MAG[0] + P[7][2]*SK_MZ[2] + P[7][18]*SK_MZ[1] + P[7][16]*SK_MZ[4] - P[7][17]*SK_MZ[3]);
|
|
Kfusion[8] = SK_MZ[0]*(P[8][21] + P[8][0]*SH_MAG[1] - P[8][1]*SH_MAG[2] + P[8][3]*SH_MAG[0] + P[8][2]*SK_MZ[2] + P[8][18]*SK_MZ[1] + P[8][16]*SK_MZ[4] - P[8][17]*SK_MZ[3]);
|
|
Kfusion[9] = SK_MZ[0]*(P[9][21] + P[9][0]*SH_MAG[1] - P[9][1]*SH_MAG[2] + P[9][3]*SH_MAG[0] + P[9][2]*SK_MZ[2] + P[9][18]*SK_MZ[1] + P[9][16]*SK_MZ[4] - P[9][17]*SK_MZ[3]);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = SK_MZ[0]*(P[10][21] + P[10][0]*SH_MAG[1] - P[10][1]*SH_MAG[2] + P[10][3]*SH_MAG[0] + P[10][2]*SK_MZ[2] + P[10][18]*SK_MZ[1] + P[10][16]*SK_MZ[4] - P[10][17]*SK_MZ[3]);
|
|
Kfusion[11] = SK_MZ[0]*(P[11][21] + P[11][0]*SH_MAG[1] - P[11][1]*SH_MAG[2] + P[11][3]*SH_MAG[0] + P[11][2]*SK_MZ[2] + P[11][18]*SK_MZ[1] + P[11][16]*SK_MZ[4] - P[11][17]*SK_MZ[3]);
|
|
Kfusion[12] = SK_MZ[0]*(P[12][21] + P[12][0]*SH_MAG[1] - P[12][1]*SH_MAG[2] + P[12][3]*SH_MAG[0] + P[12][2]*SK_MZ[2] + P[12][18]*SK_MZ[1] + P[12][16]*SK_MZ[4] - P[12][17]*SK_MZ[3]);
|
|
} else {
|
|
// zero indexes 10 to 12
|
|
zero_range(&Kfusion[0], 10, 12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
for (uint8_t index = 0; index < 3; index++) {
|
|
const uint8_t stateIndex = index + 13;
|
|
if (!dvelBiasAxisInhibit[index]) {
|
|
Kfusion[stateIndex] = SK_MZ[0]*(P[stateIndex][21] + P[stateIndex][0]*SH_MAG[1] - P[stateIndex][1]*SH_MAG[2] + P[stateIndex][3]*SH_MAG[0] + P[stateIndex][2]*SK_MZ[2] + P[stateIndex][18]*SK_MZ[1] + P[stateIndex][16]*SK_MZ[4] - P[stateIndex][17]*SK_MZ[3]);
|
|
} else {
|
|
Kfusion[stateIndex] = 0.0f;
|
|
}
|
|
}
|
|
} else {
|
|
// zero indexes 13 to 15
|
|
zero_range(&Kfusion[0], 13, 15);
|
|
}
|
|
|
|
// zero Kalman gains to inhibit magnetic field state estimation
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = SK_MZ[0]*(P[16][21] + P[16][0]*SH_MAG[1] - P[16][1]*SH_MAG[2] + P[16][3]*SH_MAG[0] + P[16][2]*SK_MZ[2] + P[16][18]*SK_MZ[1] + P[16][16]*SK_MZ[4] - P[16][17]*SK_MZ[3]);
|
|
Kfusion[17] = SK_MZ[0]*(P[17][21] + P[17][0]*SH_MAG[1] - P[17][1]*SH_MAG[2] + P[17][3]*SH_MAG[0] + P[17][2]*SK_MZ[2] + P[17][18]*SK_MZ[1] + P[17][16]*SK_MZ[4] - P[17][17]*SK_MZ[3]);
|
|
Kfusion[18] = SK_MZ[0]*(P[18][21] + P[18][0]*SH_MAG[1] - P[18][1]*SH_MAG[2] + P[18][3]*SH_MAG[0] + P[18][2]*SK_MZ[2] + P[18][18]*SK_MZ[1] + P[18][16]*SK_MZ[4] - P[18][17]*SK_MZ[3]);
|
|
Kfusion[19] = SK_MZ[0]*(P[19][21] + P[19][0]*SH_MAG[1] - P[19][1]*SH_MAG[2] + P[19][3]*SH_MAG[0] + P[19][2]*SK_MZ[2] + P[19][18]*SK_MZ[1] + P[19][16]*SK_MZ[4] - P[19][17]*SK_MZ[3]);
|
|
Kfusion[20] = SK_MZ[0]*(P[20][21] + P[20][0]*SH_MAG[1] - P[20][1]*SH_MAG[2] + P[20][3]*SH_MAG[0] + P[20][2]*SK_MZ[2] + P[20][18]*SK_MZ[1] + P[20][16]*SK_MZ[4] - P[20][17]*SK_MZ[3]);
|
|
Kfusion[21] = SK_MZ[0]*(P[21][21] + P[21][0]*SH_MAG[1] - P[21][1]*SH_MAG[2] + P[21][3]*SH_MAG[0] + P[21][2]*SK_MZ[2] + P[21][18]*SK_MZ[1] + P[21][16]*SK_MZ[4] - P[21][17]*SK_MZ[3]);
|
|
} else {
|
|
// zero indexes 16 to 21
|
|
zero_range(&Kfusion[0], 16, 21);
|
|
}
|
|
|
|
// zero Kalman gains to inhibit wind state estimation
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = SK_MZ[0]*(P[22][21] + P[22][0]*SH_MAG[1] - P[22][1]*SH_MAG[2] + P[22][3]*SH_MAG[0] + P[22][2]*SK_MZ[2] + P[22][18]*SK_MZ[1] + P[22][16]*SK_MZ[4] - P[22][17]*SK_MZ[3]);
|
|
Kfusion[23] = SK_MZ[0]*(P[23][21] + P[23][0]*SH_MAG[1] - P[23][1]*SH_MAG[2] + P[23][3]*SH_MAG[0] + P[23][2]*SK_MZ[2] + P[23][18]*SK_MZ[1] + P[23][16]*SK_MZ[4] - P[23][17]*SK_MZ[3]);
|
|
} else {
|
|
// zero indexes 22 to 23
|
|
zero_range(&Kfusion[0], 22, 23);
|
|
}
|
|
|
|
// set flags to indicate to other processes that fusion has been performed and is required on the next frame
|
|
// this can be used by other fusion processes to avoid fusing on the same frame as this expensive step
|
|
magFusePerformed = true;
|
|
}
|
|
// correct the covariance P = (I - K*H)*P
|
|
// take advantage of the empty columns in KH to reduce the
|
|
// number of operations
|
|
for (unsigned i = 0; i<=stateIndexLim; i++) {
|
|
for (unsigned j = 0; j<=3; j++) {
|
|
KH[i][j] = Kfusion[i] * H_MAG[j];
|
|
}
|
|
for (unsigned j = 4; j<=15; j++) {
|
|
KH[i][j] = 0.0f;
|
|
}
|
|
for (unsigned j = 16; j<=21; j++) {
|
|
KH[i][j] = Kfusion[i] * H_MAG[j];
|
|
}
|
|
for (unsigned j = 22; j<=23; j++) {
|
|
KH[i][j] = 0.0f;
|
|
}
|
|
}
|
|
for (unsigned j = 0; j<=stateIndexLim; j++) {
|
|
for (unsigned i = 0; i<=stateIndexLim; i++) {
|
|
ftype res = 0;
|
|
res += KH[i][0] * P[0][j];
|
|
res += KH[i][1] * P[1][j];
|
|
res += KH[i][2] * P[2][j];
|
|
res += KH[i][3] * P[3][j];
|
|
res += KH[i][16] * P[16][j];
|
|
res += KH[i][17] * P[17][j];
|
|
res += KH[i][18] * P[18][j];
|
|
res += KH[i][19] * P[19][j];
|
|
res += KH[i][20] * P[20][j];
|
|
res += KH[i][21] * P[21][j];
|
|
KHP[i][j] = res;
|
|
}
|
|
}
|
|
// Check that we are not going to drive any variances negative and skip the update if so
|
|
bool healthyFusion = true;
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
if (KHP[i][i] > P[i][i]) {
|
|
healthyFusion = false;
|
|
}
|
|
}
|
|
if (healthyFusion) {
|
|
// update the covariance matrix
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
P[i][j] = P[i][j] - KHP[i][j];
|
|
}
|
|
}
|
|
|
|
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-conditioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// correct the state vector
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
statesArray[j] = statesArray[j] - Kfusion[j] * innovMag[obsIndex];
|
|
}
|
|
|
|
// add table constraint here for faster convergence
|
|
if (have_table_earth_field && frontend->_mag_ef_limit > 0) {
|
|
MagTableConstrain();
|
|
}
|
|
|
|
stateStruct.quat.normalize();
|
|
|
|
} else {
|
|
// record bad axis
|
|
if (obsIndex == 0) {
|
|
faultStatus.bad_xmag = true;
|
|
} else if (obsIndex == 1) {
|
|
faultStatus.bad_ymag = true;
|
|
} else if (obsIndex == 2) {
|
|
faultStatus.bad_zmag = true;
|
|
}
|
|
CovarianceInit();
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
/*
|
|
* Fuse direct yaw measurements using explicit algebraic equations auto-generated from
|
|
* /AP_NavEKF3/derivation/main.py with output recorded in /AP_NavEKF3/derivation/generated/yaw_generated.cpp
|
|
* Returns true if the fusion was successful
|
|
*/
|
|
bool NavEKF3_core::fuseEulerYaw(yawFusionMethod method)
|
|
{
|
|
const ftype &q0 = stateStruct.quat[0];
|
|
const ftype &q1 = stateStruct.quat[1];
|
|
const ftype &q2 = stateStruct.quat[2];
|
|
const ftype &q3 = stateStruct.quat[3];
|
|
|
|
ftype gsfYaw, gsfYawVariance;
|
|
if (method == yawFusionMethod::GSF) {
|
|
if (!EKFGSF_getYaw(gsfYaw, gsfYawVariance)) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// yaw measurement error variance (rad^2)
|
|
ftype R_YAW;
|
|
switch (method) {
|
|
case yawFusionMethod::GPS:
|
|
R_YAW = sq(yawAngDataDelayed.yawAngErr);
|
|
break;
|
|
|
|
case yawFusionMethod::GSF:
|
|
R_YAW = gsfYawVariance;
|
|
break;
|
|
|
|
case yawFusionMethod::STATIC:
|
|
R_YAW = sq(yawAngDataStatic.yawAngErr);
|
|
break;
|
|
|
|
case yawFusionMethod::MAGNETOMETER:
|
|
case yawFusionMethod::PREDICTED:
|
|
default:
|
|
R_YAW = sq(frontend->_yawNoise);
|
|
break;
|
|
|
|
#if EK3_FEATURE_EXTERNAL_NAV
|
|
case yawFusionMethod::EXTNAV:
|
|
R_YAW = sq(MAX(extNavYawAngDataDelayed.yawAngErr, 0.05f));
|
|
break;
|
|
#endif
|
|
}
|
|
|
|
// determine if a 321 or 312 Euler sequence is best
|
|
rotationOrder order;
|
|
switch (method) {
|
|
case yawFusionMethod::GPS:
|
|
order = yawAngDataDelayed.order;
|
|
break;
|
|
|
|
case yawFusionMethod::STATIC:
|
|
order = yawAngDataStatic.order;
|
|
break;
|
|
|
|
case yawFusionMethod::MAGNETOMETER:
|
|
case yawFusionMethod::GSF:
|
|
case yawFusionMethod::PREDICTED:
|
|
default:
|
|
// determined automatically
|
|
order = (fabsF(prevTnb[0][2]) < fabsF(prevTnb[1][2])) ? rotationOrder::TAIT_BRYAN_321 : rotationOrder::TAIT_BRYAN_312;
|
|
break;
|
|
|
|
#if EK3_FEATURE_EXTERNAL_NAV
|
|
case yawFusionMethod::EXTNAV:
|
|
order = extNavYawAngDataDelayed.order;
|
|
break;
|
|
#endif
|
|
}
|
|
|
|
// calculate observation jacobian, predicted yaw and zero yaw body to earth rotation matrix
|
|
ftype yawAngPredicted;
|
|
ftype H_YAW[4];
|
|
Matrix3F Tbn_zeroYaw;
|
|
|
|
if (order == rotationOrder::TAIT_BRYAN_321) {
|
|
// calculate 321 yaw observation matrix - option A or B to avoid singularity in derivation at +-90 degrees yaw
|
|
bool canUseA = false;
|
|
const ftype SA0 = 2*q3;
|
|
const ftype SA1 = 2*q2;
|
|
const ftype SA2 = SA0*q0 + SA1*q1;
|
|
const ftype SA3 = sq(q0) + sq(q1) - sq(q2) - sq(q3);
|
|
ftype SA4, SA5_inv;
|
|
if (is_positive(sq(SA3))) {
|
|
SA4 = 1.0F/sq(SA3);
|
|
SA5_inv = sq(SA2)*SA4 + 1;
|
|
canUseA = is_positive(fabsF(SA5_inv));
|
|
}
|
|
|
|
bool canUseB = false;
|
|
const ftype SB0 = 2*q0;
|
|
const ftype SB1 = 2*q1;
|
|
const ftype SB2 = SB0*q3 + SB1*q2;
|
|
const ftype SB4 = sq(q0) + sq(q1) - sq(q2) - sq(q3);
|
|
ftype SB3, SB5_inv;
|
|
if (is_positive(sq(SB2))) {
|
|
SB3 = 1.0F/sq(SB2);
|
|
SB5_inv = SB3*sq(SB4) + 1;
|
|
canUseB = is_positive(fabsF(SB5_inv));
|
|
}
|
|
|
|
if (canUseA && (!canUseB || fabsF(SA5_inv) >= fabsF(SB5_inv))) {
|
|
const ftype SA5 = 1.0F/SA5_inv;
|
|
const ftype SA6 = 1.0F/SA3;
|
|
const ftype SA7 = SA2*SA4;
|
|
const ftype SA8 = 2*SA7;
|
|
const ftype SA9 = 2*SA6;
|
|
|
|
H_YAW[0] = SA5*(SA0*SA6 - SA8*q0);
|
|
H_YAW[1] = SA5*(SA1*SA6 - SA8*q1);
|
|
H_YAW[2] = SA5*(SA1*SA7 + SA9*q1);
|
|
H_YAW[3] = SA5*(SA0*SA7 + SA9*q0);
|
|
} else if (canUseB && (!canUseA || fabsF(SB5_inv) > fabsF(SA5_inv))) {
|
|
const ftype SB5 = 1.0F/SB5_inv;
|
|
const ftype SB6 = 1.0F/SB2;
|
|
const ftype SB7 = SB3*SB4;
|
|
const ftype SB8 = 2*SB7;
|
|
const ftype SB9 = 2*SB6;
|
|
|
|
H_YAW[0] = -SB5*(SB0*SB6 - SB8*q3);
|
|
H_YAW[1] = -SB5*(SB1*SB6 - SB8*q2);
|
|
H_YAW[2] = -SB5*(-SB1*SB7 - SB9*q2);
|
|
H_YAW[3] = -SB5*(-SB0*SB7 - SB9*q3);
|
|
} else {
|
|
return false;
|
|
}
|
|
|
|
// Get the 321 euler angles
|
|
Vector3F euler321;
|
|
stateStruct.quat.to_euler(euler321.x, euler321.y, euler321.z);
|
|
yawAngPredicted = euler321.z;
|
|
|
|
// set the yaw to zero and calculate the zero yaw rotation from body to earth frame
|
|
Tbn_zeroYaw.from_euler(euler321.x, euler321.y, 0.0f);
|
|
|
|
} else if (order == rotationOrder::TAIT_BRYAN_312) {
|
|
// calculate 312 yaw observation matrix - option A or B to avoid singularity in derivation at +-90 degrees yaw
|
|
bool canUseA = false;
|
|
const ftype SA0 = 2*q3;
|
|
const ftype SA1 = 2*q2;
|
|
const ftype SA2 = SA0*q0 - SA1*q1;
|
|
const ftype SA3 = sq(q0) - sq(q1) + sq(q2) - sq(q3);
|
|
ftype SA4, SA5_inv;
|
|
if (is_positive(sq(SA3))) {
|
|
SA4 = 1.0F/sq(SA3);
|
|
SA5_inv = sq(SA2)*SA4 + 1;
|
|
canUseA = is_positive(fabsF(SA5_inv));
|
|
}
|
|
|
|
bool canUseB = false;
|
|
const ftype SB0 = 2*q0;
|
|
const ftype SB1 = 2*q1;
|
|
const ftype SB2 = -SB0*q3 + SB1*q2;
|
|
const ftype SB4 = -sq(q0) + sq(q1) - sq(q2) + sq(q3);
|
|
ftype SB3, SB5_inv;
|
|
if (is_positive(sq(SB2))) {
|
|
SB3 = 1.0F/sq(SB2);
|
|
SB5_inv = SB3*sq(SB4) + 1;
|
|
canUseB = is_positive(fabsF(SB5_inv));
|
|
}
|
|
|
|
if (canUseA && (!canUseB || fabsF(SA5_inv) >= fabsF(SB5_inv))) {
|
|
const ftype SA5 = 1.0F/SA5_inv;
|
|
const ftype SA6 = 1.0F/SA3;
|
|
const ftype SA7 = SA2*SA4;
|
|
const ftype SA8 = 2*SA7;
|
|
const ftype SA9 = 2*SA6;
|
|
|
|
H_YAW[0] = SA5*(SA0*SA6 - SA8*q0);
|
|
H_YAW[1] = SA5*(-SA1*SA6 + SA8*q1);
|
|
H_YAW[2] = SA5*(-SA1*SA7 - SA9*q1);
|
|
H_YAW[3] = SA5*(SA0*SA7 + SA9*q0);
|
|
} else if (canUseB && (!canUseA || fabsF(SB5_inv) > fabsF(SA5_inv))) {
|
|
const ftype SB5 = 1.0F/SB5_inv;
|
|
const ftype SB6 = 1.0F/SB2;
|
|
const ftype SB7 = SB3*SB4;
|
|
const ftype SB8 = 2*SB7;
|
|
const ftype SB9 = 2*SB6;
|
|
|
|
H_YAW[0] = -SB5*(-SB0*SB6 + SB8*q3);
|
|
H_YAW[1] = -SB5*(SB1*SB6 - SB8*q2);
|
|
H_YAW[2] = -SB5*(-SB1*SB7 - SB9*q2);
|
|
H_YAW[3] = -SB5*(SB0*SB7 + SB9*q3);
|
|
} else {
|
|
return false;
|
|
}
|
|
|
|
// Get the 312 Tait Bryan rotation angles
|
|
Vector3F euler312 = stateStruct.quat.to_vector312();
|
|
yawAngPredicted = euler312.z;
|
|
|
|
// set the yaw to zero and calculate the zero yaw rotation from body to earth frame
|
|
Tbn_zeroYaw.from_euler312(euler312.x, euler312.y, 0.0f);
|
|
} else {
|
|
// order not supported
|
|
return false;
|
|
}
|
|
|
|
// Calculate the innovation
|
|
switch (method) {
|
|
case yawFusionMethod::MAGNETOMETER:
|
|
{
|
|
// Use the difference between the horizontal projection and declination to give the measured yaw
|
|
// rotate measured mag components into earth frame
|
|
Vector3F magMeasNED = Tbn_zeroYaw*magDataDelayed.mag;
|
|
ftype yawAngMeasured = wrap_PI(-atan2F(magMeasNED.y, magMeasNED.x) + MagDeclination());
|
|
innovYaw = wrap_PI(yawAngPredicted - yawAngMeasured);
|
|
break;
|
|
}
|
|
|
|
case yawFusionMethod::GPS:
|
|
innovYaw = wrap_PI(yawAngPredicted - yawAngDataDelayed.yawAng);
|
|
break;
|
|
|
|
case yawFusionMethod::STATIC:
|
|
innovYaw = wrap_PI(yawAngPredicted - yawAngDataStatic.yawAng);
|
|
break;
|
|
|
|
case yawFusionMethod::GSF:
|
|
innovYaw = wrap_PI(yawAngPredicted - gsfYaw);
|
|
break;
|
|
|
|
case yawFusionMethod::PREDICTED:
|
|
default:
|
|
innovYaw = 0.0f;
|
|
break;
|
|
|
|
#if EK3_FEATURE_EXTERNAL_NAV
|
|
case yawFusionMethod::EXTNAV:
|
|
innovYaw = wrap_PI(yawAngPredicted - extNavYawAngDataDelayed.yawAng);
|
|
break;
|
|
#endif
|
|
}
|
|
|
|
// Calculate innovation variance and Kalman gains, taking advantage of the fact that only the first 4 elements in H are non zero
|
|
ftype PH[4];
|
|
ftype varInnov = R_YAW;
|
|
for (uint8_t rowIndex=0; rowIndex<=3; rowIndex++) {
|
|
PH[rowIndex] = 0.0f;
|
|
for (uint8_t colIndex=0; colIndex<=3; colIndex++) {
|
|
PH[rowIndex] += P[rowIndex][colIndex]*H_YAW[colIndex];
|
|
}
|
|
varInnov += H_YAW[rowIndex]*PH[rowIndex];
|
|
}
|
|
ftype varInnovInv;
|
|
if (varInnov >= R_YAW) {
|
|
varInnovInv = 1.0f / varInnov;
|
|
// output numerical health status
|
|
faultStatus.bad_yaw = false;
|
|
} else {
|
|
// the calculation is badly conditioned, so we cannot perform fusion on this step
|
|
// we reset the covariance matrix and try again next measurement
|
|
CovarianceInit();
|
|
// output numerical health status
|
|
faultStatus.bad_yaw = true;
|
|
return false;
|
|
}
|
|
|
|
// calculate Kalman gain
|
|
for (uint8_t rowIndex=0; rowIndex<=stateIndexLim; rowIndex++) {
|
|
Kfusion[rowIndex] = 0.0f;
|
|
for (uint8_t colIndex=0; colIndex<=3; colIndex++) {
|
|
Kfusion[rowIndex] += P[rowIndex][colIndex]*H_YAW[colIndex];
|
|
}
|
|
Kfusion[rowIndex] *= varInnovInv;
|
|
}
|
|
|
|
// calculate the innovation test ratio
|
|
yawTestRatio = sq(innovYaw) / (sq(MAX(0.01f * (ftype)frontend->_yawInnovGate, 1.0f)) * varInnov);
|
|
|
|
// Declare the magnetometer unhealthy if the innovation test fails
|
|
if (yawTestRatio > 1.0f) {
|
|
magHealth = false;
|
|
// On the ground a large innovation could be due to large initial gyro bias or magnetic interference from nearby objects
|
|
// If we are flying, then it is more likely due to a magnetometer fault and we should not fuse the data
|
|
if (inFlight) {
|
|
return false;
|
|
}
|
|
} else {
|
|
magHealth = true;
|
|
}
|
|
|
|
// correct the covariance using P = P - K*H*P taking advantage of the fact that only the first 3 elements in H are non zero
|
|
// calculate K*H*P
|
|
for (uint8_t row = 0; row <= stateIndexLim; row++) {
|
|
for (uint8_t column = 0; column <= 3; column++) {
|
|
KH[row][column] = Kfusion[row] * H_YAW[column];
|
|
}
|
|
}
|
|
for (uint8_t row = 0; row <= stateIndexLim; row++) {
|
|
for (uint8_t column = 0; column <= stateIndexLim; column++) {
|
|
ftype tmp = KH[row][0] * P[0][column];
|
|
tmp += KH[row][1] * P[1][column];
|
|
tmp += KH[row][2] * P[2][column];
|
|
tmp += KH[row][3] * P[3][column];
|
|
KHP[row][column] = tmp;
|
|
}
|
|
}
|
|
|
|
// Check that we are not going to drive any variances negative and skip the update if so
|
|
bool healthyFusion = true;
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
if (KHP[i][i] > P[i][i]) {
|
|
healthyFusion = false;
|
|
}
|
|
}
|
|
if (healthyFusion) {
|
|
// update the covariance matrix
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
P[i][j] = P[i][j] - KHP[i][j];
|
|
}
|
|
}
|
|
|
|
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-conditioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// correct the state vector
|
|
for (uint8_t i=0; i<=stateIndexLim; i++) {
|
|
statesArray[i] -= Kfusion[i] * constrain_ftype(innovYaw, -0.5f, 0.5f);
|
|
}
|
|
stateStruct.quat.normalize();
|
|
|
|
// record fusion numerical health status
|
|
faultStatus.bad_yaw = false;
|
|
|
|
} else {
|
|
// record fusion numerical health status
|
|
faultStatus.bad_yaw = true;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
/*
|
|
* Fuse declination angle using explicit algebraic equations generated with Matlab symbolic toolbox.
|
|
* The script file used to generate these and other equations in this filter can be found here:
|
|
* https://github.com/PX4/ecl/blob/master/matlab/scripts/Inertial%20Nav%20EKF/GenerateNavFilterEquations.m
|
|
* This is used to prevent the declination of the EKF earth field states from drifting during operation without GPS
|
|
* or some other absolute position or velocity reference
|
|
*/
|
|
void NavEKF3_core::FuseDeclination(ftype declErr)
|
|
{
|
|
// declination error variance (rad^2)
|
|
const ftype R_DECL = sq(declErr);
|
|
|
|
// copy required states to local variables
|
|
ftype magN = stateStruct.earth_magfield.x;
|
|
ftype magE = stateStruct.earth_magfield.y;
|
|
|
|
// prevent bad earth field states from causing numerical errors or exceptions
|
|
if (magN < 1e-3f) {
|
|
return;
|
|
}
|
|
|
|
// Calculate observation Jacobian and Kalman gains
|
|
// Calculate intermediate variables
|
|
ftype t2 = magE*magE;
|
|
ftype t3 = magN*magN;
|
|
ftype t4 = t2+t3;
|
|
// if the horizontal magnetic field is too small, this calculation will be badly conditioned
|
|
if (t4 < 1e-4f) {
|
|
return;
|
|
}
|
|
ftype t5 = P[16][16]*t2;
|
|
ftype t6 = P[17][17]*t3;
|
|
ftype t7 = t2*t2;
|
|
ftype t8 = R_DECL*t7;
|
|
ftype t9 = t3*t3;
|
|
ftype t10 = R_DECL*t9;
|
|
ftype t11 = R_DECL*t2*t3*2.0f;
|
|
ftype t14 = P[16][17]*magE*magN;
|
|
ftype t15 = P[17][16]*magE*magN;
|
|
ftype t12 = t5+t6+t8+t10+t11-t14-t15;
|
|
ftype t13;
|
|
if (fabsF(t12) > 1e-6f) {
|
|
t13 = 1.0f / t12;
|
|
} else {
|
|
return;
|
|
}
|
|
ftype t18 = magE*magE;
|
|
ftype t19 = magN*magN;
|
|
ftype t20 = t18+t19;
|
|
ftype t21;
|
|
if (fabsF(t20) > 1e-6f) {
|
|
t21 = 1.0f/t20;
|
|
} else {
|
|
return;
|
|
}
|
|
|
|
// Calculate the observation Jacobian
|
|
// Note only 2 terms are non-zero which can be used in matrix operations for calculation of Kalman gains and covariance update to significantly reduce cost
|
|
ftype H_DECL[24] = {};
|
|
H_DECL[16] = -magE*t21;
|
|
H_DECL[17] = magN*t21;
|
|
|
|
Kfusion[0] = -t4*t13*(P[0][16]*magE-P[0][17]*magN);
|
|
Kfusion[1] = -t4*t13*(P[1][16]*magE-P[1][17]*magN);
|
|
Kfusion[2] = -t4*t13*(P[2][16]*magE-P[2][17]*magN);
|
|
Kfusion[3] = -t4*t13*(P[3][16]*magE-P[3][17]*magN);
|
|
Kfusion[4] = -t4*t13*(P[4][16]*magE-P[4][17]*magN);
|
|
Kfusion[5] = -t4*t13*(P[5][16]*magE-P[5][17]*magN);
|
|
Kfusion[6] = -t4*t13*(P[6][16]*magE-P[6][17]*magN);
|
|
Kfusion[7] = -t4*t13*(P[7][16]*magE-P[7][17]*magN);
|
|
Kfusion[8] = -t4*t13*(P[8][16]*magE-P[8][17]*magN);
|
|
Kfusion[9] = -t4*t13*(P[9][16]*magE-P[9][17]*magN);
|
|
|
|
if (!inhibitDelAngBiasStates) {
|
|
Kfusion[10] = -t4*t13*(P[10][16]*magE-P[10][17]*magN);
|
|
Kfusion[11] = -t4*t13*(P[11][16]*magE-P[11][17]*magN);
|
|
Kfusion[12] = -t4*t13*(P[12][16]*magE-P[12][17]*magN);
|
|
} else {
|
|
// zero indexes 10 to 12
|
|
zero_range(&Kfusion[0], 10, 12);
|
|
}
|
|
|
|
if (!inhibitDelVelBiasStates) {
|
|
for (uint8_t index = 0; index < 3; index++) {
|
|
const uint8_t stateIndex = index + 13;
|
|
if (!dvelBiasAxisInhibit[index]) {
|
|
Kfusion[stateIndex] = -t4*t13*(P[stateIndex][16]*magE-P[stateIndex][17]*magN);
|
|
} else {
|
|
Kfusion[stateIndex] = 0.0f;
|
|
}
|
|
}
|
|
} else {
|
|
// zero indexes 13 to 15
|
|
zero_range(&Kfusion[0], 13, 15);
|
|
}
|
|
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = -t4*t13*(P[16][16]*magE-P[16][17]*magN);
|
|
Kfusion[17] = -t4*t13*(P[17][16]*magE-P[17][17]*magN);
|
|
Kfusion[18] = -t4*t13*(P[18][16]*magE-P[18][17]*magN);
|
|
Kfusion[19] = -t4*t13*(P[19][16]*magE-P[19][17]*magN);
|
|
Kfusion[20] = -t4*t13*(P[20][16]*magE-P[20][17]*magN);
|
|
Kfusion[21] = -t4*t13*(P[21][16]*magE-P[21][17]*magN);
|
|
} else {
|
|
// zero indexes 16 to 21
|
|
zero_range(&Kfusion[0], 16, 21);
|
|
}
|
|
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = -t4*t13*(P[22][16]*magE-P[22][17]*magN);
|
|
Kfusion[23] = -t4*t13*(P[23][16]*magE-P[23][17]*magN);
|
|
} else {
|
|
// zero indexes 22 to 23
|
|
zero_range(&Kfusion[0], 22, 23);
|
|
}
|
|
|
|
// get the magnetic declination
|
|
ftype magDecAng = MagDeclination();
|
|
|
|
// Calculate the innovation
|
|
ftype innovation = atan2F(magE , magN) - magDecAng;
|
|
|
|
// limit the innovation to protect against data errors
|
|
if (innovation > 0.5f) {
|
|
innovation = 0.5f;
|
|
} else if (innovation < -0.5f) {
|
|
innovation = -0.5f;
|
|
}
|
|
|
|
// correct the covariance P = (I - K*H)*P
|
|
// take advantage of the empty columns in KH to reduce the
|
|
// number of operations
|
|
for (unsigned i = 0; i<=stateIndexLim; i++) {
|
|
for (unsigned j = 0; j<=15; j++) {
|
|
KH[i][j] = 0.0f;
|
|
}
|
|
KH[i][16] = Kfusion[i] * H_DECL[16];
|
|
KH[i][17] = Kfusion[i] * H_DECL[17];
|
|
for (unsigned j = 18; j<=23; j++) {
|
|
KH[i][j] = 0.0f;
|
|
}
|
|
}
|
|
for (unsigned j = 0; j<=stateIndexLim; j++) {
|
|
for (unsigned i = 0; i<=stateIndexLim; i++) {
|
|
KHP[i][j] = KH[i][16] * P[16][j] + KH[i][17] * P[17][j];
|
|
}
|
|
}
|
|
|
|
// Check that we are not going to drive any variances negative and skip the update if so
|
|
bool healthyFusion = true;
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
if (KHP[i][i] > P[i][i]) {
|
|
healthyFusion = false;
|
|
}
|
|
}
|
|
|
|
if (healthyFusion) {
|
|
// update the covariance matrix
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
P[i][j] = P[i][j] - KHP[i][j];
|
|
}
|
|
}
|
|
|
|
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-conditioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// correct the state vector
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
statesArray[j] = statesArray[j] - Kfusion[j] * innovation;
|
|
}
|
|
stateStruct.quat.normalize();
|
|
|
|
// record fusion health status
|
|
faultStatus.bad_decl = false;
|
|
} else {
|
|
// record fusion health status
|
|
faultStatus.bad_decl = true;
|
|
}
|
|
}
|
|
|
|
/********************************************************
|
|
* MISC FUNCTIONS *
|
|
********************************************************/
|
|
|
|
// align the NE earth magnetic field states with the published declination
|
|
void NavEKF3_core::alignMagStateDeclination()
|
|
{
|
|
// don't do this if we already have a learned magnetic field
|
|
if (magFieldLearned) {
|
|
return;
|
|
}
|
|
|
|
// get the magnetic declination
|
|
ftype magDecAng = MagDeclination();
|
|
|
|
// rotate the NE values so that the declination matches the published value
|
|
Vector3F initMagNED = stateStruct.earth_magfield;
|
|
ftype magLengthNE = initMagNED.xy().length();
|
|
stateStruct.earth_magfield.x = magLengthNE * cosF(magDecAng);
|
|
stateStruct.earth_magfield.y = magLengthNE * sinF(magDecAng);
|
|
|
|
if (!inhibitMagStates) {
|
|
// zero the corresponding state covariances if magnetic field state learning is active
|
|
ftype var_16 = P[16][16];
|
|
ftype var_17 = P[17][17];
|
|
zeroRows(P,16,17);
|
|
zeroCols(P,16,17);
|
|
P[16][16] = var_16;
|
|
P[17][17] = var_17;
|
|
|
|
// fuse the declination angle to establish covariances and prevent large swings in declination
|
|
// during initial fusion
|
|
FuseDeclination(0.1f);
|
|
|
|
}
|
|
}
|
|
|
|
// record a magnetic field state reset event
|
|
void NavEKF3_core::recordMagReset()
|
|
{
|
|
magStateResetRequest = false;
|
|
magStateInitComplete = true;
|
|
if (inFlight) {
|
|
finalInflightMagInit = true;
|
|
}
|
|
// take a snap-shot of the vertical position, quaternion and yaw innovation to use as a reference
|
|
// for post alignment checks
|
|
posDownAtLastMagReset = stateStruct.position.z;
|
|
quatAtLastMagReset = stateStruct.quat;
|
|
yawInnovAtLastMagReset = innovYaw;
|
|
}
|
|
|
|
/*
|
|
learn magnetometer biases from GPS yaw. Return true if the
|
|
resulting mag vector is close enough to the one predicted by GPS
|
|
yaw to use it for fallback
|
|
*/
|
|
bool NavEKF3_core::learnMagBiasFromGPS(void)
|
|
{
|
|
if (!have_table_earth_field) {
|
|
// we need the earth field from WMM
|
|
return false;
|
|
}
|
|
if (!inFlight) {
|
|
// don't start learning till we've started flying
|
|
return false;
|
|
}
|
|
|
|
mag_elements mag_data;
|
|
if (!storedMag.recall(mag_data, imuDataDelayed.time_ms)) {
|
|
// no mag data to correct
|
|
return false;
|
|
}
|
|
|
|
// combine yaw with current quaternion to get yaw corrected quaternion
|
|
QuaternionF quat = stateStruct.quat;
|
|
if (yawAngDataDelayed.order == rotationOrder::TAIT_BRYAN_321) {
|
|
Vector3F euler321;
|
|
quat.to_euler(euler321.x, euler321.y, euler321.z);
|
|
quat.from_euler(euler321.x, euler321.y, yawAngDataDelayed.yawAng);
|
|
} else if (yawAngDataDelayed.order == rotationOrder::TAIT_BRYAN_312) {
|
|
Vector3F euler312 = quat.to_vector312();
|
|
quat.from_vector312(euler312.x, euler312.y, yawAngDataDelayed.yawAng);
|
|
} else {
|
|
// rotation order not supported
|
|
return false;
|
|
}
|
|
|
|
// build the expected body field from orientation and table earth field
|
|
Matrix3F dcm;
|
|
quat.rotation_matrix(dcm);
|
|
Vector3F expected_body_field = dcm.transposed() * table_earth_field_ga;
|
|
|
|
// calculate error in field
|
|
Vector3F err = (expected_body_field - mag_data.mag) + stateStruct.body_magfield;
|
|
|
|
// learn body frame mag biases
|
|
stateStruct.body_magfield -= err * EK3_GPS_MAG_LEARN_RATE;
|
|
|
|
// check if error is below threshold. If it is then we can
|
|
// fallback to magnetometer on failure of external yaw
|
|
ftype err_length = err.length();
|
|
|
|
// we allow for yaw backback to compass if we have had 50 samples
|
|
// in a row below the threshold. This corresponds to 10 seconds
|
|
// for a 5Hz GPS
|
|
const uint8_t fallback_count_threshold = 50;
|
|
|
|
if (err_length > EK3_GPS_MAG_LEARN_LIMIT) {
|
|
gps_yaw_fallback_good_counter = 0;
|
|
} else if (gps_yaw_fallback_good_counter < fallback_count_threshold) {
|
|
gps_yaw_fallback_good_counter++;
|
|
}
|
|
bool ok = gps_yaw_fallback_good_counter >= fallback_count_threshold;
|
|
if (ok) {
|
|
// mark mag healthy to prevent a magTimeout when we start using it
|
|
lastHealthyMagTime_ms = imuSampleTime_ms;
|
|
}
|
|
return ok;
|
|
}
|
|
|
|
// Reset states using yaw from EKF-GSF and velocity and position from GPS
|
|
bool NavEKF3_core::EKFGSF_resetMainFilterYaw(bool emergency_reset)
|
|
{
|
|
// Don't do a reset unless permitted by the EK3_GSF_USE_MASK and EK3_GSF_RUN_MASK parameter masks
|
|
if ((yawEstimator == nullptr)
|
|
|| !(frontend->_gsfUseMask & (1U<<core_index))) {
|
|
return false;
|
|
};
|
|
|
|
// limit the number of emergency resets
|
|
if (emergency_reset && (EKFGSF_yaw_reset_count >= frontend->_gsfResetMaxCount)) {
|
|
return false;
|
|
}
|
|
|
|
ftype yawEKFGSF, yawVarianceEKFGSF;
|
|
if (EKFGSF_getYaw(yawEKFGSF, yawVarianceEKFGSF)) {
|
|
// keep roll and pitch and reset yaw
|
|
rotationOrder order;
|
|
bestRotationOrder(order);
|
|
resetQuatStateYawOnly(yawEKFGSF, yawVarianceEKFGSF, order);
|
|
|
|
// record the emergency reset event
|
|
EKFGSF_yaw_reset_request_ms = 0;
|
|
EKFGSF_yaw_reset_ms = imuSampleTime_ms;
|
|
EKFGSF_yaw_reset_count++;
|
|
|
|
if ((frontend->sources.getYawSource() == AP_NavEKF_Source::SourceYaw::GSF) ||
|
|
!use_compass() || (dal.compass().get_num_enabled() == 0)) {
|
|
GCS_SEND_TEXT(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw aligned using GPS",(unsigned)imu_index);
|
|
} else {
|
|
GCS_SEND_TEXT(MAV_SEVERITY_WARNING, "EKF3 IMU%u emergency yaw reset",(unsigned)imu_index);
|
|
}
|
|
|
|
// Fail the magnetomer so it doesn't get used and pull the yaw away from the correct value
|
|
if (emergency_reset) {
|
|
allMagSensorsFailed = true;
|
|
}
|
|
|
|
// record the yaw reset event
|
|
recordYawReset();
|
|
|
|
// reset velocity and position states to GPS - if yaw is fixed then the filter should start to operate correctly
|
|
ResetVelocity(resetDataSource::DEFAULT);
|
|
ResetPosition(resetDataSource::DEFAULT);
|
|
|
|
// reset test ratios that are reported to prevent a race condition with the external state machine requesting the reset
|
|
velTestRatio = 0.0f;
|
|
posTestRatio = 0.0f;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
// returns true on success and populates yaw (in radians) and yawVariance (rad^2)
|
|
bool NavEKF3_core::EKFGSF_getYaw(ftype &yaw, ftype &yawVariance) const
|
|
{
|
|
// return immediately if no yaw estimator
|
|
if (yawEstimator == nullptr) {
|
|
return false;
|
|
}
|
|
|
|
ftype velInnovLength;
|
|
if (yawEstimator->getYawData(yaw, yawVariance) &&
|
|
is_positive(yawVariance) &&
|
|
yawVariance < sq(radians(GSF_YAW_ACCURACY_THRESHOLD_DEG)) &&
|
|
(assume_zero_sideslip() || (yawEstimator->getVelInnovLength(velInnovLength) && velInnovLength < frontend->maxYawEstVelInnov))) {
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
void NavEKF3_core::resetQuatStateYawOnly(ftype yaw, ftype yawVariance, rotationOrder order)
|
|
{
|
|
QuaternionF quatBeforeReset = stateStruct.quat;
|
|
|
|
// check if we should use a 321 or 312 Rotation order and update the quaternion
|
|
// states using the preferred yaw definition
|
|
stateStruct.quat.inverse().rotation_matrix(prevTnb);
|
|
Vector3F eulerAngles;
|
|
if (order == rotationOrder::TAIT_BRYAN_321) {
|
|
// rolled more than pitched so use 321 rotation order
|
|
stateStruct.quat.to_euler(eulerAngles.x, eulerAngles.y, eulerAngles.z);
|
|
stateStruct.quat.from_euler(eulerAngles.x, eulerAngles.y, yaw);
|
|
} else if (order == rotationOrder::TAIT_BRYAN_312) {
|
|
// pitched more than rolled so use 312 rotation order
|
|
eulerAngles = stateStruct.quat.to_vector312();
|
|
stateStruct.quat.from_vector312(eulerAngles.x, eulerAngles.y, yaw);
|
|
} else {
|
|
// rotation order not supported
|
|
return;
|
|
}
|
|
|
|
// Update the rotation matrix
|
|
stateStruct.quat.inverse().rotation_matrix(prevTnb);
|
|
|
|
ftype deltaYaw = wrap_PI(yaw - eulerAngles.z);
|
|
|
|
// calculate the change in the quaternion state and apply it to the output history buffer
|
|
QuaternionF quat_delta = stateStruct.quat / quatBeforeReset;
|
|
StoreQuatRotate(quat_delta);
|
|
|
|
// assume tilt uncertainty split equally between roll and pitch
|
|
Vector3F angleErrVarVec = Vector3F(0.5 * tiltErrorVariance, 0.5 * tiltErrorVariance, yawVariance);
|
|
CovariancePrediction(&angleErrVarVec);
|
|
|
|
// record the yaw reset event
|
|
yawResetAngle += deltaYaw;
|
|
lastYawReset_ms = imuSampleTime_ms;
|
|
|
|
// record the yaw reset event
|
|
recordYawReset();
|
|
|
|
// clear all pending yaw reset requests
|
|
gpsYawResetRequest = false;
|
|
magYawResetRequest = false;
|
|
|
|
}
|