mirror of https://github.com/ArduPilot/ardupilot
833 lines
39 KiB
C++
833 lines
39 KiB
C++
/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-
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#include <AP_HAL/AP_HAL.h>
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#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150
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#include "AP_NavEKF3.h"
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#include "AP_NavEKF3_core.h"
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#include <AP_AHRS/AP_AHRS.h>
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#include <AP_Vehicle/AP_Vehicle.h>
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extern const AP_HAL::HAL& hal;
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/********************************************************
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* RESET FUNCTIONS *
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********************************************************/
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// Reset velocity states to last GPS measurement if available or to zero if in constant position mode or if PV aiding is not absolute
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// Do not reset vertical velocity using GPS as there is baro alt available to constrain drift
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void NavEKF3_core::ResetVelocity(void)
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{
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// Store the position before the reset so that we can record the reset delta
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velResetNE.x = stateStruct.velocity.x;
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velResetNE.y = stateStruct.velocity.y;
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// reset the corresponding covariances
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zeroRows(P,4,5);
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zeroCols(P,4,5);
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if (PV_AidingMode != AID_ABSOLUTE) {
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stateStruct.velocity.zero();
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// set the variances using the measurement noise parameter
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P[5][5] = P[4][4] = sq(frontend->_gpsHorizVelNoise);
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} else {
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// reset horizontal velocity states to the GPS velocity if available
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if ((imuSampleTime_ms - lastTimeGpsReceived_ms < 250 && velResetSource == DEFAULT) || velResetSource == GPS) {
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stateStruct.velocity.x = gpsDataNew.vel.x;
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stateStruct.velocity.y = gpsDataNew.vel.y;
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// set the variances using the reported GPS speed accuracy
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P[5][5] = P[4][4] = sq(MAX(frontend->_gpsHorizVelNoise,gpsSpdAccuracy));
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// clear the timeout flags and counters
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velTimeout = false;
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lastVelPassTime_ms = imuSampleTime_ms;
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} else {
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stateStruct.velocity.x = 0.0f;
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stateStruct.velocity.y = 0.0f;
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// set the variances using the likely speed range
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P[5][5] = P[4][4] = sq(25.0f);
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// clear the timeout flags and counters
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velTimeout = false;
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lastVelPassTime_ms = imuSampleTime_ms;
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}
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].velocity.x = stateStruct.velocity.x;
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storedOutput[i].velocity.y = stateStruct.velocity.y;
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}
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outputDataNew.velocity.x = stateStruct.velocity.x;
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outputDataNew.velocity.y = stateStruct.velocity.y;
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outputDataDelayed.velocity.x = stateStruct.velocity.x;
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outputDataDelayed.velocity.y = stateStruct.velocity.y;
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// Calculate the position jump due to the reset
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velResetNE.x = stateStruct.velocity.x - velResetNE.x;
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velResetNE.y = stateStruct.velocity.y - velResetNE.y;
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// store the time of the reset
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lastVelReset_ms = imuSampleTime_ms;
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// clear reset data source preference
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velResetSource = DEFAULT;
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}
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// resets position states to last GPS measurement or to zero if in constant position mode
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void NavEKF3_core::ResetPosition(void)
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{
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// Store the position before the reset so that we can record the reset delta
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posResetNE.x = stateStruct.position.x;
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posResetNE.y = stateStruct.position.y;
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// reset the corresponding covariances
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zeroRows(P,7,8);
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zeroCols(P,7,8);
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if (PV_AidingMode != AID_ABSOLUTE) {
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// reset all position state history to the last known position
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stateStruct.position.x = lastKnownPositionNE.x;
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stateStruct.position.y = lastKnownPositionNE.y;
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// set the variances using the position measurement noise parameter
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P[7][7] = P[8][8] = sq(frontend->_gpsHorizPosNoise);
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} else {
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// Use GPS data as first preference if fresh data is available
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if ((imuSampleTime_ms - lastTimeGpsReceived_ms < 250 && posResetSource == DEFAULT) || posResetSource == GPS) {
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// write to state vector and compensate for offset between last GPS measurement and the EKF time horizon
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stateStruct.position.x = gpsDataNew.pos.x + 0.001f*gpsDataNew.vel.x*(float(imuDataDelayed.time_ms) - float(gpsDataNew.time_ms));
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stateStruct.position.y = gpsDataNew.pos.y + 0.001f*gpsDataNew.vel.y*(float(imuDataDelayed.time_ms) - float(gpsDataNew.time_ms));
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// set the variances using the position measurement noise parameter
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P[7][7] = P[8][8] = sq(MAX(gpsPosAccuracy,frontend->_gpsHorizPosNoise));
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// clear the timeout flags and counters
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posTimeout = false;
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lastPosPassTime_ms = imuSampleTime_ms;
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} else if ((imuSampleTime_ms - rngBcnLast3DmeasTime_ms < 250 && posResetSource == DEFAULT) || posResetSource == RNGBCN) {
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// use the range beacon data as a second preference
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stateStruct.position.x = receiverPos.x;
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stateStruct.position.y = receiverPos.y;
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// set the variances from the beacon alignment filter
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P[7][7] = receiverPosCov[0][0];
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P[8][8] = receiverPosCov[1][1];
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// clear the timeout flags and counters
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rngBcnTimeout = false;
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lastRngBcnPassTime_ms = imuSampleTime_ms;
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}
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].position.x = stateStruct.position.x;
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storedOutput[i].position.y = stateStruct.position.y;
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}
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outputDataNew.position.x = stateStruct.position.x;
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outputDataNew.position.y = stateStruct.position.y;
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outputDataDelayed.position.x = stateStruct.position.x;
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outputDataDelayed.position.y = stateStruct.position.y;
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// Calculate the position jump due to the reset
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posResetNE.x = stateStruct.position.x - posResetNE.x;
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posResetNE.y = stateStruct.position.y - posResetNE.y;
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// store the time of the reset
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lastPosReset_ms = imuSampleTime_ms;
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// clear reset source preference
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posResetSource = DEFAULT;
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}
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// reset the vertical position state using the last height measurement
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void NavEKF3_core::ResetHeight(void)
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{
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// Store the position before the reset so that we can record the reset delta
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posResetD = stateStruct.position.z;
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// write to the state vector
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stateStruct.position.z = -hgtMea;
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outputDataNew.position.z = stateStruct.position.z;
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outputDataDelayed.position.z = stateStruct.position.z;
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// reset the terrain state height
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if (onGround) {
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// assume vehicle is sitting on the ground
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terrainState = stateStruct.position.z + rngOnGnd;
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} else {
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// can make no assumption other than vehicle is not below ground level
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terrainState = MAX(stateStruct.position.z + rngOnGnd , terrainState);
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].position.z = stateStruct.position.z;
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}
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// Calculate the position jump due to the reset
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posResetD = stateStruct.position.z - posResetD;
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// store the time of the reset
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lastPosResetD_ms = imuSampleTime_ms;
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// clear the timeout flags and counters
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hgtTimeout = false;
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lastHgtPassTime_ms = imuSampleTime_ms;
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// reset the corresponding covariances
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zeroRows(P,9,9);
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zeroCols(P,9,9);
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// set the variances to the measurement variance
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P[9][9] = posDownObsNoise;
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// Reset the vertical velocity state using GPS vertical velocity if we are airborne
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// Check that GPS vertical velocity data is available and can be used
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if (inFlight && !gpsNotAvailable && frontend->_fusionModeGPS == 0) {
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stateStruct.velocity.z = gpsDataNew.vel.z;
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} else if (onGround) {
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stateStruct.velocity.z = 0.0f;
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}
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for (uint8_t i=0; i<imu_buffer_length; i++) {
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storedOutput[i].velocity.z = stateStruct.velocity.z;
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}
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outputDataNew.velocity.z = stateStruct.velocity.z;
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outputDataDelayed.velocity.z = stateStruct.velocity.z;
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// reset the corresponding covariances
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zeroRows(P,6,6);
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zeroCols(P,6,6);
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// set the variances to the measurement variance
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P[6][6] = sq(frontend->_gpsVertVelNoise);
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}
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// Zero the EKF height datum
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// Return true if the height datum reset has been performed
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bool NavEKF3_core::resetHeightDatum(void)
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{
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if (activeHgtSource == HGT_SOURCE_RNG) {
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// by definition the height datum is at ground level so cannot perform the reset
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return false;
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}
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// record the old height estimate
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float oldHgt = -stateStruct.position.z;
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// reset the barometer so that it reads zero at the current height
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frontend->_baro.update_calibration();
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// reset the height state
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stateStruct.position.z = 0.0f;
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// adjust the height of the EKF origin so that the origin plus baro height before and after the reset is the same
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if (validOrigin) {
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EKF_origin.alt += oldHgt*100;
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}
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// adjust the terrain state
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terrainState += oldHgt;
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return true;
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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 velocity, position and height measurements
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void NavEKF3_core::SelectVelPosFusion()
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{
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// Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz
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// If so, don't fuse measurements on this time step to reduce frame over-runs
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// Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements
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if (magFusePerformed && dtIMUavg < 0.005f && !posVelFusionDelayed) {
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posVelFusionDelayed = true;
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return;
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} else {
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posVelFusionDelayed = false;
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}
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// read GPS data from the sensor and check for new data in the buffer
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readGpsData();
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gpsDataToFuse = storedGPS.recall(gpsDataDelayed,imuDataDelayed.time_ms);
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// Determine if we need to fuse position and velocity data on this time step
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if (gpsDataToFuse && PV_AidingMode == AID_ABSOLUTE) {
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// Don't fuse velocity data if GPS doesn't support it
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if (frontend->_fusionModeGPS <= 1) {
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fuseVelData = true;
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} else {
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fuseVelData = false;
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}
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fusePosData = true;
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// correct GPS data for position offset of antenna phase centre relative to the IMU
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Vector3f posOffsetBody = _ahrs->get_gps().get_antenna_offset(gpsDataDelayed.sensor_idx) - accelPosOffset;
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if (!posOffsetBody.is_zero()) {
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if (fuseVelData) {
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// TODO use a filtered angular rate with a group delay that matches the GPS delay
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Vector3f angRate = imuDataDelayed.delAng * (1.0f/imuDataDelayed.delAngDT);
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Vector3f velOffsetBody = angRate % posOffsetBody;
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Vector3f velOffsetEarth = prevTnb.mul_transpose(velOffsetBody);
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gpsDataDelayed.vel -= velOffsetEarth;
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}
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Vector3f posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
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gpsDataDelayed.pos.x -= posOffsetEarth.x;
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gpsDataDelayed.pos.y -= posOffsetEarth.y;
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gpsDataDelayed.hgt += posOffsetEarth.z;
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}
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} else {
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fuseVelData = false;
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fusePosData = false;
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}
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// we have GPS data to fuse and a request to align the yaw using the GPS course
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if (gpsYawResetRequest) {
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realignYawGPS();
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}
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// Select height data to be fused from the available baro, range finder and GPS sources
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selectHeightForFusion();
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// If we are operating without any aiding, fuse in the last known position
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// to constrain tilt drift. This assumes a non-manoeuvring vehicle
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// Do this to coincide with the height fusion
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if (fuseHgtData && PV_AidingMode == AID_NONE) {
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gpsDataDelayed.vel.zero();
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gpsDataDelayed.pos.x = lastKnownPositionNE.x;
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gpsDataDelayed.pos.y = lastKnownPositionNE.y;
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fusePosData = true;
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fuseVelData = false;
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}
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// perform fusion
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if (fuseVelData || fusePosData || fuseHgtData) {
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FuseVelPosNED();
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// clear the flags to prevent repeated fusion of the same data
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fuseVelData = false;
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fuseHgtData = false;
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fusePosData = false;
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}
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}
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// fuse selected position, velocity and height measurements
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void NavEKF3_core::FuseVelPosNED()
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{
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// start performance timer
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hal.util->perf_begin(_perf_FuseVelPosNED);
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// health is set bad until test passed
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velHealth = false;
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posHealth = false;
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hgtHealth = false;
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// declare variables used to check measurement errors
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Vector3f velInnov;
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// declare variables used to control access to arrays
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bool fuseData[6] = {false,false,false,false,false,false};
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uint8_t stateIndex;
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uint8_t obsIndex;
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// declare variables used by state and covariance update calculations
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Vector6 R_OBS; // Measurement variances used for fusion
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Vector6 R_OBS_DATA_CHECKS; // Measurement variances used for data checks only
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Vector6 observation;
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float SK;
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// perform sequential fusion of GPS measurements. This assumes that the
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// errors in the different velocity and position components are
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// uncorrelated which is not true, however in the absence of covariance
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// data from the GPS receiver it is the only assumption we can make
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// so we might as well take advantage of the computational efficiencies
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// associated with sequential fusion
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if (fuseVelData || fusePosData || fuseHgtData) {
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// form the observation vector
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observation[0] = gpsDataDelayed.vel.x;
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observation[1] = gpsDataDelayed.vel.y;
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observation[2] = gpsDataDelayed.vel.z;
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observation[3] = gpsDataDelayed.pos.x;
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observation[4] = gpsDataDelayed.pos.y;
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observation[5] = -hgtMea;
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// calculate additional error in GPS position caused by manoeuvring
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float posErr = frontend->gpsPosVarAccScale * accNavMag;
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// estimate the GPS Velocity, GPS horiz position and height measurement variances.
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// Use different errors if operating without external aiding using an assumed position or velocity of zero
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if (PV_AidingMode == AID_NONE) {
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if (tiltAlignComplete && motorsArmed) {
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// This is a compromise between corrections for gyro errors and reducing effect of manoeuvre accelerations on tilt estimate
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R_OBS[0] = sq(constrain_float(frontend->_noaidHorizNoise, 0.5f, 50.0f));
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} else {
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// Use a smaller value to give faster initial alignment
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R_OBS[0] = sq(0.5f);
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}
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R_OBS[1] = R_OBS[0];
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R_OBS[2] = R_OBS[0];
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R_OBS[3] = R_OBS[0];
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R_OBS[4] = R_OBS[0];
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for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
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} else {
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if (gpsSpdAccuracy > 0.0f) {
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// use GPS receivers reported speed accuracy if available and floor at value set by GPS velocity noise parameter
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R_OBS[0] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsHorizVelNoise, 50.0f));
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R_OBS[2] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsVertVelNoise, 50.0f));
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} else {
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// calculate additional error in GPS velocity caused by manoeuvring
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R_OBS[0] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
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R_OBS[2] = sq(constrain_float(frontend->_gpsVertVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsDVelVarAccScale * accNavMag);
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}
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R_OBS[1] = R_OBS[0];
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// Use GPS reported position accuracy if available and floor at value set by GPS position noise parameter
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if (gpsPosAccuracy > 0.0f) {
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R_OBS[3] = sq(constrain_float(gpsPosAccuracy, frontend->_gpsHorizPosNoise, 100.0f));
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} else {
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R_OBS[3] = sq(constrain_float(frontend->_gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
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}
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R_OBS[4] = R_OBS[3];
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// For data integrity checks we use the same measurement variances as used to calculate the Kalman gains for all measurements except GPS horizontal velocity
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// For horizontal GPs velocity we don't want the acceptance radius to increase with reported GPS accuracy so we use a value based on best GPs perfomrance
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// plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
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for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
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}
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R_OBS[5] = posDownObsNoise;
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for (uint8_t i=3; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
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// if vertical GPS velocity data and an independent height source is being used, check to see if the GPS vertical velocity and altimeter
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// innovations have the same sign and are outside limits. If so, then it is likely aliasing is affecting
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// the accelerometers and we should disable the GPS and barometer innovation consistency checks.
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if (useGpsVertVel && fuseVelData && (frontend->_altSource != 2)) {
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// calculate innovations for height and vertical GPS vel measurements
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float hgtErr = stateStruct.position.z - observation[5];
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float velDErr = stateStruct.velocity.z - observation[2];
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// check if they are the same sign and both more than 3-sigma out of bounds
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if ((hgtErr*velDErr > 0.0f) && (sq(hgtErr) > 9.0f * (P[9][9] + R_OBS_DATA_CHECKS[5])) && (sq(velDErr) > 9.0f * (P[6][6] + R_OBS_DATA_CHECKS[2]))) {
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badIMUdata = true;
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} else {
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badIMUdata = false;
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}
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}
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// calculate innovations and check GPS data validity using an innovation consistency check
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// test position measurements
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if (fusePosData) {
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// test horizontal position measurements
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innovVelPos[3] = stateStruct.position.x - observation[3];
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innovVelPos[4] = stateStruct.position.y - observation[4];
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varInnovVelPos[3] = P[7][7] + R_OBS_DATA_CHECKS[3];
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varInnovVelPos[4] = P[8][8] + R_OBS_DATA_CHECKS[4];
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// apply an innovation consistency threshold test, but don't fail if bad IMU data
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float maxPosInnov2 = sq(MAX(0.01f * (float)frontend->_gpsPosInnovGate, 1.0f))*(varInnovVelPos[3] + varInnovVelPos[4]);
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posTestRatio = (sq(innovVelPos[3]) + sq(innovVelPos[4])) / maxPosInnov2;
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posHealth = ((posTestRatio < 1.0f) || badIMUdata);
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// use position data if healthy or timed out
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if (PV_AidingMode == AID_NONE) {
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posHealth = true;
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lastPosPassTime_ms = imuSampleTime_ms;
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} else if (posHealth || posTimeout) {
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posHealth = true;
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lastPosPassTime_ms = imuSampleTime_ms;
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// if timed out or outside the specified uncertainty radius, reset to the GPS
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if (posTimeout || ((P[8][8] + P[7][7]) > sq(float(frontend->_gpsGlitchRadiusMax)))) {
|
|
// reset the position to the current GPS position
|
|
ResetPosition();
|
|
// reset the velocity to the GPS velocity
|
|
ResetVelocity();
|
|
// don't fuse GPS data on this time step
|
|
fusePosData = false;
|
|
fuseVelData = false;
|
|
// Reset the position variances and corresponding covariances to a value that will pass the checks
|
|
zeroRows(P,7,8);
|
|
zeroCols(P,7,8);
|
|
P[7][7] = sq(float(0.5f*frontend->_gpsGlitchRadiusMax));
|
|
P[8][8] = P[7][7];
|
|
// Reset the normalised innovation to avoid failing the bad fusion tests
|
|
posTestRatio = 0.0f;
|
|
velTestRatio = 0.0f;
|
|
}
|
|
} else {
|
|
posHealth = false;
|
|
}
|
|
}
|
|
|
|
// test velocity measurements
|
|
if (fuseVelData) {
|
|
// test velocity measurements
|
|
uint8_t imax = 2;
|
|
// Don't fuse vertical velocity observations if inhibited by the user or if we are using synthetic data
|
|
if (frontend->_fusionModeGPS >= 1 || PV_AidingMode != AID_ABSOLUTE) {
|
|
imax = 1;
|
|
}
|
|
float innovVelSumSq = 0; // sum of squares of velocity innovations
|
|
float varVelSum = 0; // sum of velocity innovation variances
|
|
for (uint8_t i = 0; i<=imax; i++) {
|
|
// velocity states start at index 4
|
|
stateIndex = i + 4;
|
|
// calculate innovations using blended and single IMU predicted states
|
|
velInnov[i] = stateStruct.velocity[i] - observation[i]; // blended
|
|
// calculate innovation variance
|
|
varInnovVelPos[i] = P[stateIndex][stateIndex] + R_OBS_DATA_CHECKS[i];
|
|
// sum the innovation and innovation variances
|
|
innovVelSumSq += sq(velInnov[i]);
|
|
varVelSum += varInnovVelPos[i];
|
|
}
|
|
// apply an innovation consistency threshold test, but don't fail if bad IMU data
|
|
// calculate the test ratio
|
|
velTestRatio = innovVelSumSq / (varVelSum * sq(MAX(0.01f * (float)frontend->_gpsVelInnovGate, 1.0f)));
|
|
// fail if the ratio is greater than 1
|
|
velHealth = ((velTestRatio < 1.0f) || badIMUdata);
|
|
// use velocity data if healthy, timed out, or in constant position mode
|
|
if (velHealth || velTimeout) {
|
|
velHealth = true;
|
|
// restart the timeout count
|
|
lastVelPassTime_ms = imuSampleTime_ms;
|
|
// If we are doing full aiding and velocity fusion times out, reset to the GPS velocity
|
|
if (PV_AidingMode == AID_ABSOLUTE && velTimeout) {
|
|
// reset the velocity to the GPS velocity
|
|
ResetVelocity();
|
|
// don't fuse GPS velocity data on this time step
|
|
fuseVelData = false;
|
|
// Reset the normalised innovation to avoid failing the bad fusion tests
|
|
velTestRatio = 0.0f;
|
|
}
|
|
} else {
|
|
velHealth = false;
|
|
}
|
|
}
|
|
|
|
// test height measurements
|
|
if (fuseHgtData) {
|
|
// calculate height innovations
|
|
innovVelPos[5] = stateStruct.position.z - observation[5];
|
|
varInnovVelPos[5] = P[9][9] + R_OBS_DATA_CHECKS[5];
|
|
// calculate the innovation consistency test ratio
|
|
hgtTestRatio = sq(innovVelPos[5]) / (sq(MAX(0.01f * (float)frontend->_hgtInnovGate, 1.0f)) * varInnovVelPos[5]);
|
|
// fail if the ratio is > 1, but don't fail if bad IMU data
|
|
hgtHealth = ((hgtTestRatio < 1.0f) || badIMUdata);
|
|
// Fuse height data if healthy or timed out or in constant position mode
|
|
if (hgtHealth || hgtTimeout || (PV_AidingMode == AID_NONE && onGround)) {
|
|
// Calculate a filtered value to be used by pre-flight health checks
|
|
// We need to filter because wind gusts can generate significant baro noise and we want to be able to detect bias errors in the inertial solution
|
|
if (onGround) {
|
|
float dtBaro = (imuSampleTime_ms - lastHgtPassTime_ms)*1.0e-3f;
|
|
const float hgtInnovFiltTC = 2.0f;
|
|
float alpha = constrain_float(dtBaro/(dtBaro+hgtInnovFiltTC),0.0f,1.0f);
|
|
hgtInnovFiltState += (innovVelPos[5]-hgtInnovFiltState)*alpha;
|
|
} else {
|
|
hgtInnovFiltState = 0.0f;
|
|
}
|
|
|
|
// if timed out, reset the height
|
|
if (hgtTimeout) {
|
|
ResetHeight();
|
|
}
|
|
|
|
// If we have got this far then declare the height data as healthy and reset the timeout counter
|
|
hgtHealth = true;
|
|
lastHgtPassTime_ms = imuSampleTime_ms;
|
|
}
|
|
}
|
|
|
|
// set range for sequential fusion of velocity and position measurements depending on which data is available and its health
|
|
if (fuseVelData && velHealth) {
|
|
fuseData[0] = true;
|
|
fuseData[1] = true;
|
|
if (useGpsVertVel) {
|
|
fuseData[2] = true;
|
|
}
|
|
}
|
|
if (fusePosData && posHealth) {
|
|
fuseData[3] = true;
|
|
fuseData[4] = true;
|
|
}
|
|
if (fuseHgtData && hgtHealth) {
|
|
fuseData[5] = true;
|
|
}
|
|
|
|
// fuse measurements sequentially
|
|
for (obsIndex=0; obsIndex<=5; obsIndex++) {
|
|
if (fuseData[obsIndex]) {
|
|
stateIndex = 4 + obsIndex;
|
|
// calculate the measurement innovation, using states from a different time coordinate if fusing height data
|
|
// adjust scaling on GPS measurement noise variances if not enough satellites
|
|
if (obsIndex <= 2)
|
|
{
|
|
innovVelPos[obsIndex] = stateStruct.velocity[obsIndex] - observation[obsIndex];
|
|
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
|
|
}
|
|
else if (obsIndex == 3 || obsIndex == 4) {
|
|
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
|
|
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
|
|
} else if (obsIndex == 5) {
|
|
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
|
|
const float gndMaxBaroErr = 4.0f;
|
|
const float gndBaroInnovFloor = -0.5f;
|
|
|
|
if(getTouchdownExpected()) {
|
|
// when a touchdown is expected, floor the barometer innovation at gndBaroInnovFloor
|
|
// constrain the correction between 0 and gndBaroInnovFloor+gndMaxBaroErr
|
|
// this function looks like this:
|
|
// |/
|
|
//---------|---------
|
|
// ____/|
|
|
// / |
|
|
// / |
|
|
innovVelPos[5] += constrain_float(-innovVelPos[5]+gndBaroInnovFloor, 0.0f, gndBaroInnovFloor+gndMaxBaroErr);
|
|
}
|
|
}
|
|
|
|
// calculate the Kalman gain and calculate innovation variances
|
|
varInnovVelPos[obsIndex] = P[stateIndex][stateIndex] + R_OBS[obsIndex];
|
|
SK = 1.0f/varInnovVelPos[obsIndex];
|
|
for (uint8_t i= 0; i<=15; i++) {
|
|
Kfusion[i] = P[i][stateIndex]*SK;
|
|
}
|
|
|
|
// inhibit magnetic field state estimation by setting Kalman gains to zero
|
|
if (!inhibitMagStates) {
|
|
for (uint8_t i = 16; i<=21; i++) {
|
|
Kfusion[i] = P[i][stateIndex]*SK;
|
|
}
|
|
} else {
|
|
for (uint8_t i = 16; i<=21; i++) {
|
|
Kfusion[i] = 0.0f;
|
|
}
|
|
}
|
|
|
|
// inhibit wind state estimation by setting Kalman gains to zero
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = P[22][stateIndex]*SK;
|
|
Kfusion[23] = P[23][stateIndex]*SK;
|
|
} else {
|
|
Kfusion[22] = 0.0f;
|
|
Kfusion[23] = 0.0f;
|
|
}
|
|
|
|
// update the covariance - take advantage of direct observation of a single state at index = stateIndex to reduce computations
|
|
// this is a numerically optimised implementation of standard equation P = (I - K*H)*P;
|
|
for (uint8_t i= 0; i<=stateIndexLim; i++) {
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++)
|
|
{
|
|
KHP[i][j] = Kfusion[i] * P[stateIndex][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-condiioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// update states and renormalise the quaternions
|
|
for (uint8_t i = 0; i<=stateIndexLim; i++) {
|
|
statesArray[i] = statesArray[i] - Kfusion[i] * innovVelPos[obsIndex];
|
|
}
|
|
stateStruct.quat.normalize();
|
|
|
|
// record good fusion status
|
|
if (obsIndex == 0) {
|
|
faultStatus.bad_nvel = false;
|
|
} else if (obsIndex == 1) {
|
|
faultStatus.bad_evel = false;
|
|
} else if (obsIndex == 2) {
|
|
faultStatus.bad_dvel = false;
|
|
} else if (obsIndex == 3) {
|
|
faultStatus.bad_npos = false;
|
|
} else if (obsIndex == 4) {
|
|
faultStatus.bad_epos = false;
|
|
} else if (obsIndex == 5) {
|
|
faultStatus.bad_dpos = false;
|
|
}
|
|
} else {
|
|
// record bad fusion status
|
|
if (obsIndex == 0) {
|
|
faultStatus.bad_nvel = true;
|
|
} else if (obsIndex == 1) {
|
|
faultStatus.bad_evel = true;
|
|
} else if (obsIndex == 2) {
|
|
faultStatus.bad_dvel = true;
|
|
} else if (obsIndex == 3) {
|
|
faultStatus.bad_npos = true;
|
|
} else if (obsIndex == 4) {
|
|
faultStatus.bad_epos = true;
|
|
} else if (obsIndex == 5) {
|
|
faultStatus.bad_dpos = true;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// stop performance timer
|
|
hal.util->perf_end(_perf_FuseVelPosNED);
|
|
}
|
|
|
|
/********************************************************
|
|
* MISC FUNCTIONS *
|
|
********************************************************/
|
|
|
|
// select the height measurement to be fused from the available baro, range finder and GPS sources
|
|
void NavEKF3_core::selectHeightForFusion()
|
|
{
|
|
// Read range finder data and check for new data in the buffer
|
|
// This data is used by both height and optical flow fusion processing
|
|
readRangeFinder();
|
|
rangeDataToFuse = storedRange.recall(rangeDataDelayed,imuDataDelayed.time_ms);
|
|
|
|
// correct range data for the body frame position offset relative to the IMU
|
|
// the corrected reading is the reading that would have been taken if the sensor was
|
|
// co-located with the IMU
|
|
if (rangeDataToFuse) {
|
|
Vector3f posOffsetBody = frontend->_rng.get_pos_offset(rangeDataDelayed.sensor_idx) - accelPosOffset;
|
|
if (!posOffsetBody.is_zero()) {
|
|
Vector3f posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
|
|
rangeDataDelayed.rng += posOffsetEarth.z / prevTnb.c.z;
|
|
}
|
|
}
|
|
|
|
// read baro height data from the sensor and check for new data in the buffer
|
|
readBaroData();
|
|
baroDataToFuse = storedBaro.recall(baroDataDelayed, imuDataDelayed.time_ms);
|
|
|
|
// select height source
|
|
if (((frontend->_useRngSwHgt > 0) || (frontend->_altSource == 1)) && (imuSampleTime_ms - rngValidMeaTime_ms < 500)) {
|
|
if (frontend->_altSource == 1) {
|
|
// always use range finder
|
|
activeHgtSource = HGT_SOURCE_RNG;
|
|
} else {
|
|
// determine if we are above or below the height switch region
|
|
float rangeMaxUse = 1e-4f * (float)frontend->_rng.max_distance_cm() * (float)frontend->_useRngSwHgt;
|
|
bool aboveUpperSwHgt = (terrainState - stateStruct.position.z) > rangeMaxUse;
|
|
bool belowLowerSwHgt = (terrainState - stateStruct.position.z) < 0.7f * rangeMaxUse;
|
|
|
|
// If the terrain height is consistent and we are moving slowly, then it can be
|
|
// used as a height reference in combination with a range finder
|
|
// apply a hysteresis to the speed check to prevent rapid switching
|
|
bool dontTrustTerrain, trustTerrain;
|
|
if (filterStatus.flags.horiz_vel) {
|
|
// We can use the velocity estimate
|
|
float horizSpeed = norm(stateStruct.velocity.x, stateStruct.velocity.y);
|
|
dontTrustTerrain = (horizSpeed > frontend->_useRngSwSpd) || !terrainHgtStable;
|
|
float trust_spd_trigger = MAX((frontend->_useRngSwSpd - 1.0f),(frontend->_useRngSwSpd * 0.5f));
|
|
trustTerrain = (horizSpeed < trust_spd_trigger) && terrainHgtStable;
|
|
} else {
|
|
// We can't use the velocity estimate
|
|
dontTrustTerrain = !terrainHgtStable;
|
|
trustTerrain = terrainHgtStable;
|
|
}
|
|
|
|
/*
|
|
* Switch between range finder and primary height source using height above ground and speed thresholds with
|
|
* hysteresis to avoid rapid switching. Using range finder for height requires a consistent terrain height
|
|
* which cannot be assumed if the vehicle is moving horizontally.
|
|
*/
|
|
if ((aboveUpperSwHgt || dontTrustTerrain) && (activeHgtSource == HGT_SOURCE_RNG)) {
|
|
// cannot trust terrain or range finder so stop using range finder height
|
|
if (frontend->_altSource == 0) {
|
|
activeHgtSource = HGT_SOURCE_BARO;
|
|
} else if (frontend->_altSource == 2) {
|
|
activeHgtSource = HGT_SOURCE_GPS;
|
|
}
|
|
} else if (belowLowerSwHgt && trustTerrain && (activeHgtSource != HGT_SOURCE_RNG)) {
|
|
// reliable terrain and range finder so start using range finder height
|
|
activeHgtSource = HGT_SOURCE_RNG;
|
|
}
|
|
}
|
|
} else if ((frontend->_altSource == 2) && ((imuSampleTime_ms - lastTimeGpsReceived_ms) < 500) && validOrigin && gpsAccuracyGood) {
|
|
activeHgtSource = HGT_SOURCE_GPS;
|
|
} else if ((frontend->_altSource == 3) && validOrigin && rngBcnGoodToAlign) {
|
|
activeHgtSource = HGT_SOURCE_BCN;
|
|
} else {
|
|
activeHgtSource = HGT_SOURCE_BARO;
|
|
}
|
|
|
|
// Use Baro alt as a fallback if we lose range finder or GPS
|
|
bool lostRngHgt = ((activeHgtSource == HGT_SOURCE_RNG) && ((imuSampleTime_ms - rngValidMeaTime_ms) > 500));
|
|
bool lostGpsHgt = ((activeHgtSource == HGT_SOURCE_GPS) && ((imuSampleTime_ms - lastTimeGpsReceived_ms) > 2000));
|
|
if (lostRngHgt || lostGpsHgt) {
|
|
activeHgtSource = HGT_SOURCE_BARO;
|
|
}
|
|
|
|
// if there is new baro data to fuse, calculate filtered baro data required by other processes
|
|
if (baroDataToFuse) {
|
|
// calculate offset to baro data that enables us to switch to Baro height use during operation
|
|
if (activeHgtSource != HGT_SOURCE_BARO) {
|
|
calcFiltBaroOffset();
|
|
}
|
|
// filtered baro data used to provide a reference for takeoff
|
|
// it is is reset to last height measurement on disarming in performArmingChecks()
|
|
if (!getTakeoffExpected()) {
|
|
const float gndHgtFiltTC = 0.5f;
|
|
const float dtBaro = frontend->hgtAvg_ms*1.0e-3f;
|
|
float alpha = constrain_float(dtBaro / (dtBaro+gndHgtFiltTC),0.0f,1.0f);
|
|
meaHgtAtTakeOff += (baroDataDelayed.hgt-meaHgtAtTakeOff)*alpha;
|
|
}
|
|
}
|
|
|
|
// calculate offset to GPS height data that enables us to switch to GPS height during operation
|
|
if (gpsDataToFuse && (activeHgtSource != HGT_SOURCE_GPS)) {
|
|
calcFiltGpsHgtOffset();
|
|
}
|
|
|
|
// Select the height measurement source
|
|
if (rangeDataToFuse && (activeHgtSource == HGT_SOURCE_RNG)) {
|
|
// using range finder data
|
|
// correct for tilt using a flat earth model
|
|
if (prevTnb.c.z >= 0.7) {
|
|
// calculate height above ground
|
|
hgtMea = MAX(rangeDataDelayed.rng * prevTnb.c.z, rngOnGnd);
|
|
// correct for terrain position relative to datum
|
|
hgtMea -= terrainState;
|
|
// enable fusion
|
|
fuseHgtData = true;
|
|
// set the observation noise
|
|
posDownObsNoise = sq(constrain_float(frontend->_rngNoise, 0.1f, 10.0f));
|
|
// add uncertainty created by terrain gradient and vehicle tilt
|
|
posDownObsNoise += sq(rangeDataDelayed.rng * frontend->_terrGradMax) * MAX(0.0f , (1.0f - sq(prevTnb.c.z)));
|
|
} else {
|
|
// disable fusion if tilted too far
|
|
fuseHgtData = false;
|
|
}
|
|
} else if (gpsDataToFuse && (activeHgtSource == HGT_SOURCE_GPS)) {
|
|
// using GPS data
|
|
hgtMea = gpsDataDelayed.hgt;
|
|
// enable fusion
|
|
fuseHgtData = true;
|
|
// set the observation noise using receiver reported accuracy or the horizontal noise scaled for typical VDOP/HDOP ratio
|
|
if (gpsHgtAccuracy > 0.0f) {
|
|
posDownObsNoise = sq(constrain_float(gpsHgtAccuracy, 1.5f * frontend->_gpsHorizPosNoise, 100.0f));
|
|
} else {
|
|
posDownObsNoise = sq(constrain_float(1.5f * frontend->_gpsHorizPosNoise, 0.1f, 10.0f));
|
|
}
|
|
} else if (baroDataToFuse && (activeHgtSource == HGT_SOURCE_BARO)) {
|
|
// using Baro data
|
|
hgtMea = baroDataDelayed.hgt - baroHgtOffset;
|
|
// enable fusion
|
|
fuseHgtData = true;
|
|
// set the observation noise
|
|
posDownObsNoise = sq(constrain_float(frontend->_baroAltNoise, 0.1f, 10.0f));
|
|
// reduce weighting (increase observation noise) on baro if we are likely to be in ground effect
|
|
if (getTakeoffExpected() || getTouchdownExpected()) {
|
|
posDownObsNoise *= frontend->gndEffectBaroScaler;
|
|
}
|
|
// If we are in takeoff mode, the height measurement is limited to be no less than the measurement at start of takeoff
|
|
// This prevents negative baro disturbances due to copter downwash corrupting the EKF altitude during initial ascent
|
|
if (motorsArmed && getTakeoffExpected()) {
|
|
hgtMea = MAX(hgtMea, meaHgtAtTakeOff);
|
|
}
|
|
} else {
|
|
fuseHgtData = false;
|
|
}
|
|
|
|
// If we haven't fused height data for a while, then declare the height data as being timed out
|
|
// set timeout period based on whether we have vertical GPS velocity available to constrain drift
|
|
hgtRetryTime_ms = (useGpsVertVel && !velTimeout) ? frontend->hgtRetryTimeMode0_ms : frontend->hgtRetryTimeMode12_ms;
|
|
if (imuSampleTime_ms - lastHgtPassTime_ms > hgtRetryTime_ms) {
|
|
hgtTimeout = true;
|
|
} else {
|
|
hgtTimeout = false;
|
|
}
|
|
}
|
|
|
|
#endif // HAL_CPU_CLASS
|