mirror of https://github.com/ArduPilot/ardupilot
722 lines
33 KiB
C++
722 lines
33 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_NavEKF2.h"
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#include "AP_NavEKF2_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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#include <stdio.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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/********************************************************
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* FUSE MEASURED_DATA *
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********************************************************/
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// select fusion of optical flow measurements
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void NavEKF2_core::SelectFlowFusion()
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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 && !optFlowFusionDelayed) {
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optFlowFusionDelayed = true;
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return;
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} else {
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optFlowFusionDelayed = false;
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}
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// start performance timer
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hal.util->perf_begin(_perf_FuseOptFlow);
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// Perform Data Checks
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// Check if the optical flow data is still valid
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flowDataValid = ((imuSampleTime_ms - flowValidMeaTime_ms) < 1000);
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// Check if the optical flow sensor has timed out
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bool flowSensorTimeout = ((imuSampleTime_ms - flowValidMeaTime_ms) > 5000);
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// Check if the fusion has timed out (flow measurements have been rejected for too long)
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bool flowFusionTimeout = ((imuSampleTime_ms - prevFlowFuseTime_ms) > 5000);
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// check is the terrain offset estimate is still valid
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gndOffsetValid = ((imuSampleTime_ms - gndHgtValidTime_ms) < 5000);
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// Perform tilt check
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bool tiltOK = (Tnb_flow.c.z > frontend->DCM33FlowMin);
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// Constrain measurements to zero if we are using optical flow and are on the ground
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if (frontend->_fusionModeGPS == 3 && !takeOffDetected && isAiding) {
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ofDataDelayed.flowRadXYcomp.zero();
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ofDataDelayed.flowRadXY.zero();
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flowDataValid = true;
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}
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// If the flow measurements have been rejected for too long and we are relying on them, then revert to constant position mode
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if ((flowSensorTimeout || flowFusionTimeout) && PV_AidingMode == AID_RELATIVE) {
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PV_AidingMode = AID_NONE;
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// reset the velocity
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ResetVelocity();
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// store the current position to be used to as a sythetic position measurement
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lastKnownPositionNE.x = stateStruct.position.x;
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lastKnownPositionNE.y = stateStruct.position.y;
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// reset the position
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ResetPosition();
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}
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// if we do have valid flow measurements, fuse data into a 1-state EKF to estimate terrain height
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// we don't do terrain height estimation in optical flow only mode as the ground becomes our zero height reference
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if ((flowDataToFuse || rangeDataToFuse) && tiltOK) {
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// fuse optical flow data into the terrain estimator if available and if there is no range data (range data is better)
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fuseOptFlowData = (flowDataToFuse && !rangeDataToFuse);
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// Estimate the terrain offset (runs a one state EKF)
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EstimateTerrainOffset();
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}
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// Fuse optical flow data into the main filter if not excessively tilted and we are in the correct mode
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if (flowDataToFuse && tiltOK && PV_AidingMode == AID_RELATIVE)
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{
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// Set the flow noise used by the fusion processes
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R_LOS = sq(MAX(frontend->_flowNoise, 0.05f));
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// Fuse the optical flow X and Y axis data into the main filter sequentially
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FuseOptFlow();
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// reset flag to indicate that no new flow data is available for fusion
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flowDataToFuse = false;
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}
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// stop the performance timer
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hal.util->perf_end(_perf_FuseOptFlow);
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}
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/*
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Estimation of terrain offset using a single state EKF
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The filter can fuse motion compensated optiocal flow rates and range finder measurements
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*/
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void NavEKF2_core::EstimateTerrainOffset()
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{
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// start performance timer
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hal.util->perf_begin(_perf_TerrainOffset);
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// constrain height above ground to be above range measured on ground
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float heightAboveGndEst = MAX((terrainState - stateStruct.position.z), rngOnGnd);
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// calculate a predicted LOS rate squared
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float velHorizSq = sq(stateStruct.velocity.x) + sq(stateStruct.velocity.y);
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float losRateSq = velHorizSq / sq(heightAboveGndEst);
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// don't update terrain offset state if there is no range finder and not generating enough LOS rate, or without GPS, as it is poorly observable
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if (!rangeDataToFuse && (gpsNotAvailable || PV_AidingMode == AID_RELATIVE || velHorizSq < 25.0f || losRateSq < 0.01f)) {
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inhibitGndState = true;
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} else {
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inhibitGndState = false;
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// record the time we last updated the terrain offset state
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gndHgtValidTime_ms = imuSampleTime_ms;
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// propagate ground position state noise each time this is called using the difference in position since the last observations and an RMS gradient assumption
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// limit distance to prevent intialisation afer bad gps causing bad numerical conditioning
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float distanceTravelledSq = sq(stateStruct.position[0] - prevPosN) + sq(stateStruct.position[1] - prevPosE);
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distanceTravelledSq = MIN(distanceTravelledSq, 100.0f);
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prevPosN = stateStruct.position[0];
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prevPosE = stateStruct.position[1];
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// in addition to a terrain gradient error model, we also have the growth in uncertainty due to the copters vertical velocity
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float timeLapsed = MIN(0.001f * (imuSampleTime_ms - timeAtLastAuxEKF_ms), 1.0f);
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float Pincrement = (distanceTravelledSq * sq(0.01f*float(frontend->gndGradientSigma))) + sq(timeLapsed)*P[5][5];
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Popt += Pincrement;
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timeAtLastAuxEKF_ms = imuSampleTime_ms;
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// fuse range finder data
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if (rangeDataToFuse) {
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// predict range
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float predRngMeas = MAX((terrainState - stateStruct.position[2]),rngOnGnd) / Tnb_flow.c.z;
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// Copy required states to local variable names
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float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
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float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
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float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
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float q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
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// Set range finder measurement noise variance. TODO make this a function of range and tilt to allow for sensor, alignment and AHRS errors
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float R_RNG = frontend->_rngNoise;
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// calculate Kalman gain
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float SK_RNG = sq(q0) - sq(q1) - sq(q2) + sq(q3);
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float K_RNG = Popt/(SK_RNG*(R_RNG + Popt/sq(SK_RNG)));
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// Calculate the innovation variance for data logging
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varInnovRng = (R_RNG + Popt/sq(SK_RNG));
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// constrain terrain height to be below the vehicle
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terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
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// Calculate the measurement innovation
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innovRng = predRngMeas - rangeDataDelayed.rng;
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// calculate the innovation consistency test ratio
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auxRngTestRatio = sq(innovRng) / (sq(MAX(0.01f * (float)frontend->_rngInnovGate, 1.0f)) * varInnovRng);
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// Check the innovation for consistency and don't fuse if > 5Sigma
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if ((sq(innovRng)*SK_RNG) < 25.0f)
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{
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// correct the state
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terrainState -= K_RNG * innovRng;
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// constrain the state
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terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
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// correct the covariance
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Popt = Popt - sq(Popt)/(SK_RNG*(R_RNG + Popt/sq(SK_RNG))*(sq(q0) - sq(q1) - sq(q2) + sq(q3)));
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// prevent the state variance from becoming negative
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Popt = MAX(Popt,0.0f);
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}
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}
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if (fuseOptFlowData) {
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Vector3f relVelSensor; // velocity of sensor relative to ground in sensor axes
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float losPred; // predicted optical flow angular rate measurement
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float q0 = stateStruct.quat[0]; // quaternion at optical flow measurement time
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float q1 = stateStruct.quat[1]; // quaternion at optical flow measurement time
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float q2 = stateStruct.quat[2]; // quaternion at optical flow measurement time
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float q3 = stateStruct.quat[3]; // quaternion at optical flow measurement time
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float K_OPT;
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float H_OPT;
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// predict range to centre of image
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float flowRngPred = MAX((terrainState - stateStruct.position[2]),rngOnGnd) / Tnb_flow.c.z;
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// constrain terrain height to be below the vehicle
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terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
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// calculate relative velocity in sensor frame
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relVelSensor = Tnb_flow*stateStruct.velocity;
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// divide velocity by range, subtract body rates and apply scale factor to
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// get predicted sensed angular optical rates relative to X and Y sensor axes
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losPred = relVelSensor.length()/flowRngPred;
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// calculate innovations
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auxFlowObsInnov = losPred - sqrtf(sq(flowRadXYcomp[0]) + sq(flowRadXYcomp[1]));
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// calculate observation jacobian
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float t3 = sq(q0);
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float t4 = sq(q1);
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float t5 = sq(q2);
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float t6 = sq(q3);
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float t10 = q0*q3*2.0f;
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float t11 = q1*q2*2.0f;
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float t14 = t3+t4-t5-t6;
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float t15 = t14*stateStruct.velocity.x;
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float t16 = t10+t11;
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float t17 = t16*stateStruct.velocity.y;
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float t18 = q0*q2*2.0f;
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float t19 = q1*q3*2.0f;
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float t20 = t18-t19;
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float t21 = t20*stateStruct.velocity.z;
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float t2 = t15+t17-t21;
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float t7 = t3-t4-t5+t6;
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float t8 = stateStruct.position[2]-terrainState;
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float t9 = 1.0f/sq(t8);
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float t24 = t3-t4+t5-t6;
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float t25 = t24*stateStruct.velocity.y;
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float t26 = t10-t11;
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float t27 = t26*stateStruct.velocity.x;
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float t28 = q0*q1*2.0f;
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float t29 = q2*q3*2.0f;
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float t30 = t28+t29;
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float t31 = t30*stateStruct.velocity.z;
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float t12 = t25-t27+t31;
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float t13 = sq(t7);
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float t22 = sq(t2);
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float t23 = 1.0f/(t8*t8*t8);
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float t32 = sq(t12);
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H_OPT = 0.5f*(t13*t22*t23*2.0f+t13*t23*t32*2.0f)/sqrtf(t9*t13*t22+t9*t13*t32);
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// calculate innovation variances
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auxFlowObsInnovVar = H_OPT*Popt*H_OPT + R_LOS;
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// calculate Kalman gain
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K_OPT = Popt*H_OPT/auxFlowObsInnovVar;
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// calculate the innovation consistency test ratio
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auxFlowTestRatio = sq(auxFlowObsInnov) / (sq(MAX(0.01f * (float)frontend->_flowInnovGate, 1.0f)) * auxFlowObsInnovVar);
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// don't fuse if optical flow data is outside valid range
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if (MAX(flowRadXY[0],flowRadXY[1]) < frontend->_maxFlowRate) {
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// correct the state
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terrainState -= K_OPT * auxFlowObsInnov;
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// constrain the state
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terrainState = MAX(terrainState, stateStruct.position[2] + rngOnGnd);
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// correct the covariance
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Popt = Popt - K_OPT * H_OPT * Popt;
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// prevent the state variances from becoming negative
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Popt = MAX(Popt,0.0f);
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}
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}
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}
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// stop the performance timer
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hal.util->perf_end(_perf_TerrainOffset);
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}
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/*
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* Fuse angular motion compensated optical flow rates using explicit algebraic equations generated with Matlab symbolic toolbox.
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* The script file used to generate these and other equations in this filter can be found here:
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* https://github.com/priseborough/InertialNav/blob/master/derivations/RotationVectorAttitudeParameterisation/GenerateNavFilterEquations.m
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* Requires a valid terrain height estimate.
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*/
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void NavEKF2_core::FuseOptFlow()
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{
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Vector24 H_LOS;
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Vector3f relVelSensor;
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Vector14 SH_LOS;
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Vector2 losPred;
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// Copy required states to local variable names
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float q0 = stateStruct.quat[0];
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float q1 = stateStruct.quat[1];
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float q2 = stateStruct.quat[2];
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float q3 = stateStruct.quat[3];
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float vn = stateStruct.velocity.x;
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float ve = stateStruct.velocity.y;
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float vd = stateStruct.velocity.z;
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float pd = stateStruct.position.z;
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// constrain height above ground to be above range measured on ground
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float heightAboveGndEst = MAX((terrainState - pd), rngOnGnd);
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float ptd = pd + heightAboveGndEst;
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// Calculate common expressions for observation jacobians
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SH_LOS[0] = sq(q0) - sq(q1) - sq(q2) + sq(q3);
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SH_LOS[1] = vn*(sq(q0) + sq(q1) - sq(q2) - sq(q3)) - vd*(2*q0*q2 - 2*q1*q3) + ve*(2*q0*q3 + 2*q1*q2);
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SH_LOS[2] = ve*(sq(q0) - sq(q1) + sq(q2) - sq(q3)) + vd*(2*q0*q1 + 2*q2*q3) - vn*(2*q0*q3 - 2*q1*q2);
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SH_LOS[3] = 1/(pd - ptd);
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SH_LOS[4] = vd*SH_LOS[0] - ve*(2*q0*q1 - 2*q2*q3) + vn*(2*q0*q2 + 2*q1*q3);
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SH_LOS[5] = 2.0f*q0*q2 - 2.0f*q1*q3;
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SH_LOS[6] = 2.0f*q0*q1 + 2.0f*q2*q3;
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SH_LOS[7] = q0*q0;
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SH_LOS[8] = q1*q1;
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SH_LOS[9] = q2*q2;
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SH_LOS[10] = q3*q3;
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SH_LOS[11] = q0*q3*2.0f;
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SH_LOS[12] = pd-ptd;
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SH_LOS[13] = 1.0f/(SH_LOS[12]*SH_LOS[12]);
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// Fuse X and Y axis measurements sequentially assuming observation errors are uncorrelated
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for (uint8_t obsIndex=0; obsIndex<=1; obsIndex++) { // fuse X axis data first
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// calculate range from ground plain to centre of sensor fov assuming flat earth
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float range = constrain_float((heightAboveGndEst/Tnb_flow.c.z),rngOnGnd,1000.0f);
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// calculate relative velocity in sensor frame
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relVelSensor = Tnb_flow*stateStruct.velocity;
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// divide velocity by range to get predicted angular LOS rates relative to X and Y axes
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losPred[0] = relVelSensor.y/range;
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losPred[1] = -relVelSensor.x/range;
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// calculate observation jacobians and Kalman gains
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memset(&H_LOS[0], 0, sizeof(H_LOS));
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if (obsIndex == 0) {
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H_LOS[0] = SH_LOS[3]*SH_LOS[2]*SH_LOS[6]-SH_LOS[3]*SH_LOS[0]*SH_LOS[4];
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H_LOS[1] = SH_LOS[3]*SH_LOS[2]*SH_LOS[5];
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H_LOS[2] = SH_LOS[3]*SH_LOS[0]*SH_LOS[1];
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H_LOS[3] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[11]-q1*q2*2.0f);
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H_LOS[4] = -SH_LOS[3]*SH_LOS[0]*(SH_LOS[7]-SH_LOS[8]+SH_LOS[9]-SH_LOS[10]);
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H_LOS[5] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[6];
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H_LOS[8] = SH_LOS[2]*SH_LOS[0]*SH_LOS[13];
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float t2 = SH_LOS[3];
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float t3 = SH_LOS[0];
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float t4 = SH_LOS[2];
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float t5 = SH_LOS[6];
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float t100 = t2 * t3 * t5;
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float t6 = SH_LOS[4];
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float t7 = t2*t3*t6;
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float t9 = t2*t4*t5;
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float t8 = t7-t9;
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float t10 = q0*q3*2.0f;
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float t21 = q1*q2*2.0f;
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float t11 = t10-t21;
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float t101 = t2 * t3 * t11;
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float t12 = pd-ptd;
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float t13 = 1.0f/(t12*t12);
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float t104 = t3 * t4 * t13;
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float t14 = SH_LOS[5];
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float t102 = t2 * t4 * t14;
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float t15 = SH_LOS[1];
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float t103 = t2 * t3 * t15;
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float t16 = q0*q0;
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float t17 = q1*q1;
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float t18 = q2*q2;
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float t19 = q3*q3;
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float t20 = t16-t17+t18-t19;
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float t105 = t2 * t3 * t20;
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float t22 = P[1][1]*t102;
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float t23 = P[3][0]*t101;
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float t24 = P[8][0]*t104;
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float t25 = P[1][0]*t102;
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float t26 = P[2][0]*t103;
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float t63 = P[0][0]*t8;
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float t64 = P[5][0]*t100;
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float t65 = P[4][0]*t105;
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float t27 = t23+t24+t25+t26-t63-t64-t65;
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float t28 = P[3][3]*t101;
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float t29 = P[8][3]*t104;
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float t30 = P[1][3]*t102;
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float t31 = P[2][3]*t103;
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float t67 = P[0][3]*t8;
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float t68 = P[5][3]*t100;
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float t69 = P[4][3]*t105;
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float t32 = t28+t29+t30+t31-t67-t68-t69;
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float t33 = t101*t32;
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float t34 = P[3][8]*t101;
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float t35 = P[8][8]*t104;
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float t36 = P[1][8]*t102;
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float t37 = P[2][8]*t103;
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float t70 = P[0][8]*t8;
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float t71 = P[5][8]*t100;
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float t72 = P[4][8]*t105;
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float t38 = t34+t35+t36+t37-t70-t71-t72;
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float t39 = t104*t38;
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float t40 = P[3][1]*t101;
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float t41 = P[8][1]*t104;
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float t42 = P[2][1]*t103;
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float t73 = P[0][1]*t8;
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float t74 = P[5][1]*t100;
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float t75 = P[4][1]*t105;
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float t43 = t22+t40+t41+t42-t73-t74-t75;
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float t44 = t102*t43;
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float t45 = P[3][2]*t101;
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float t46 = P[8][2]*t104;
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float t47 = P[1][2]*t102;
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float t48 = P[2][2]*t103;
|
|
float t76 = P[0][2]*t8;
|
|
float t77 = P[5][2]*t100;
|
|
float t78 = P[4][2]*t105;
|
|
float t49 = t45+t46+t47+t48-t76-t77-t78;
|
|
float t50 = t103*t49;
|
|
float t51 = P[3][5]*t101;
|
|
float t52 = P[8][5]*t104;
|
|
float t53 = P[1][5]*t102;
|
|
float t54 = P[2][5]*t103;
|
|
float t79 = P[0][5]*t8;
|
|
float t80 = P[5][5]*t100;
|
|
float t81 = P[4][5]*t105;
|
|
float t55 = t51+t52+t53+t54-t79-t80-t81;
|
|
float t56 = P[3][4]*t101;
|
|
float t57 = P[8][4]*t104;
|
|
float t58 = P[1][4]*t102;
|
|
float t59 = P[2][4]*t103;
|
|
float t83 = P[0][4]*t8;
|
|
float t84 = P[5][4]*t100;
|
|
float t85 = P[4][4]*t105;
|
|
float t60 = t56+t57+t58+t59-t83-t84-t85;
|
|
float t66 = t8*t27;
|
|
float t82 = t100*t55;
|
|
float t86 = t105*t60;
|
|
float t61 = R_LOS+t33+t39+t44+t50-t66-t82-t86;
|
|
float t62 = 1.0f/t61;
|
|
|
|
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
|
|
if (t61 > R_LOS) {
|
|
t62 = 1.0f/t61;
|
|
faultStatus.bad_yflow = false;
|
|
} else {
|
|
t61 = 0.0f;
|
|
t62 = 1.0f/R_LOS;
|
|
faultStatus.bad_yflow = true;
|
|
return;
|
|
}
|
|
varInnovOptFlow[0] = t61;
|
|
|
|
// calculate innovation for X axis observation
|
|
innovOptFlow[0] = losPred[0] - ofDataDelayed.flowRadXYcomp.x;
|
|
|
|
// calculate Kalman gains for X-axis observation
|
|
Kfusion[0] = t62*(-P[0][0]*t8-P[0][5]*t100+P[0][3]*t101+P[0][1]*t102+P[0][2]*t103+P[0][8]*t104-P[0][4]*t105);
|
|
Kfusion[1] = t62*(t22-P[1][0]*t8-P[1][5]*t100+P[1][3]*t101+P[1][2]*t103+P[1][8]*t104-P[1][4]*t105);
|
|
Kfusion[2] = t62*(t48-P[2][0]*t8-P[2][5]*t100+P[2][3]*t101+P[2][1]*t102+P[2][8]*t104-P[2][4]*t105);
|
|
Kfusion[3] = t62*(t28-P[3][0]*t8-P[3][5]*t100+P[3][1]*t102+P[3][2]*t103+P[3][8]*t104-P[3][4]*t105);
|
|
Kfusion[4] = t62*(-t85-P[4][0]*t8-P[4][5]*t100+P[4][3]*t101+P[4][1]*t102+P[4][2]*t103+P[4][8]*t104);
|
|
Kfusion[5] = t62*(-t80-P[5][0]*t8+P[5][3]*t101+P[5][1]*t102+P[5][2]*t103+P[5][8]*t104-P[5][4]*t105);
|
|
Kfusion[6] = t62*(-P[6][0]*t8-P[6][5]*t100+P[6][3]*t101+P[6][1]*t102+P[6][2]*t103+P[6][8]*t104-P[6][4]*t105);
|
|
Kfusion[7] = t62*(-P[7][0]*t8-P[7][5]*t100+P[7][3]*t101+P[7][1]*t102+P[7][2]*t103+P[7][8]*t104-P[7][4]*t105);
|
|
Kfusion[8] = t62*(t35-P[8][0]*t8-P[8][5]*t100+P[8][3]*t101+P[8][1]*t102+P[8][2]*t103-P[8][4]*t105);
|
|
Kfusion[9] = t62*(-P[9][0]*t8-P[9][5]*t100+P[9][3]*t101+P[9][1]*t102+P[9][2]*t103+P[9][8]*t104-P[9][4]*t105);
|
|
Kfusion[10] = t62*(-P[10][0]*t8-P[10][5]*t100+P[10][3]*t101+P[10][1]*t102+P[10][2]*t103+P[10][8]*t104-P[10][4]*t105);
|
|
Kfusion[11] = t62*(-P[11][0]*t8-P[11][5]*t100+P[11][3]*t101+P[11][1]*t102+P[11][2]*t103+P[11][8]*t104-P[11][4]*t105);
|
|
Kfusion[12] = t62*(-P[12][0]*t8-P[12][5]*t100+P[12][3]*t101+P[12][1]*t102+P[12][2]*t103+P[12][8]*t104-P[12][4]*t105);
|
|
Kfusion[13] = t62*(-P[13][0]*t8-P[13][5]*t100+P[13][3]*t101+P[13][1]*t102+P[13][2]*t103+P[13][8]*t104-P[13][4]*t105);
|
|
Kfusion[14] = t62*(-P[14][0]*t8-P[14][5]*t100+P[14][3]*t101+P[14][1]*t102+P[14][2]*t103+P[14][8]*t104-P[14][4]*t105);
|
|
Kfusion[15] = t62*(-P[15][0]*t8-P[15][5]*t100+P[15][3]*t101+P[15][1]*t102+P[15][2]*t103+P[15][8]*t104-P[15][4]*t105);
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = t62*(-P[22][0]*t8-P[22][5]*t100+P[22][3]*t101+P[22][1]*t102+P[22][2]*t103+P[22][8]*t104-P[22][4]*t105);
|
|
Kfusion[23] = t62*(-P[23][0]*t8-P[23][5]*t100+P[23][3]*t101+P[23][1]*t102+P[23][2]*t103+P[23][8]*t104-P[23][4]*t105);
|
|
} else {
|
|
Kfusion[22] = 0.0f;
|
|
Kfusion[23] = 0.0f;
|
|
}
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = t62*(-P[16][0]*t8-P[16][5]*t100+P[16][3]*t101+P[16][1]*t102+P[16][2]*t103+P[16][8]*t104-P[16][4]*t105);
|
|
Kfusion[17] = t62*(-P[17][0]*t8-P[17][5]*t100+P[17][3]*t101+P[17][1]*t102+P[17][2]*t103+P[17][8]*t104-P[17][4]*t105);
|
|
Kfusion[18] = t62*(-P[18][0]*t8-P[18][5]*t100+P[18][3]*t101+P[18][1]*t102+P[18][2]*t103+P[18][8]*t104-P[18][4]*t105);
|
|
Kfusion[19] = t62*(-P[19][0]*t8-P[19][5]*t100+P[19][3]*t101+P[19][1]*t102+P[19][2]*t103+P[19][8]*t104-P[19][4]*t105);
|
|
Kfusion[20] = t62*(-P[20][0]*t8-P[20][5]*t100+P[20][3]*t101+P[20][1]*t102+P[20][2]*t103+P[20][8]*t104-P[20][4]*t105);
|
|
Kfusion[21] = t62*(-P[21][0]*t8-P[21][5]*t100+P[21][3]*t101+P[21][1]*t102+P[21][2]*t103+P[21][8]*t104-P[21][4]*t105);
|
|
} else {
|
|
for (uint8_t i = 16; i <= 21; i++) {
|
|
Kfusion[i] = 0.0f;
|
|
}
|
|
}
|
|
|
|
} else {
|
|
|
|
H_LOS[0] = -SH_LOS[3]*SH_LOS[6]*SH_LOS[1];
|
|
H_LOS[1] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[4]-SH_LOS[3]*SH_LOS[1]*SH_LOS[5];
|
|
H_LOS[2] = SH_LOS[3]*SH_LOS[2]*SH_LOS[0];
|
|
H_LOS[3] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[7]+SH_LOS[8]-SH_LOS[9]-SH_LOS[10]);
|
|
H_LOS[4] = SH_LOS[3]*SH_LOS[0]*(SH_LOS[11]+q1*q2*2.0f);
|
|
H_LOS[5] = -SH_LOS[3]*SH_LOS[0]*SH_LOS[5];
|
|
H_LOS[8] = -SH_LOS[0]*SH_LOS[1]*SH_LOS[13];
|
|
|
|
float t2 = SH_LOS[3];
|
|
float t3 = SH_LOS[0];
|
|
float t4 = SH_LOS[1];
|
|
float t5 = SH_LOS[5];
|
|
float t100 = t2 * t3 * t5;
|
|
float t6 = SH_LOS[4];
|
|
float t7 = t2*t3*t6;
|
|
float t8 = t2*t4*t5;
|
|
float t9 = t7+t8;
|
|
float t10 = q0*q3*2.0f;
|
|
float t11 = q1*q2*2.0f;
|
|
float t12 = t10+t11;
|
|
float t101 = t2 * t3 * t12;
|
|
float t13 = pd-ptd;
|
|
float t14 = 1.0f/(t13*t13);
|
|
float t104 = t3 * t4 * t14;
|
|
float t15 = SH_LOS[6];
|
|
float t105 = t2 * t4 * t15;
|
|
float t16 = SH_LOS[2];
|
|
float t102 = t2 * t3 * t16;
|
|
float t17 = q0*q0;
|
|
float t18 = q1*q1;
|
|
float t19 = q2*q2;
|
|
float t20 = q3*q3;
|
|
float t21 = t17+t18-t19-t20;
|
|
float t103 = t2 * t3 * t21;
|
|
float t22 = P[0][0]*t105;
|
|
float t23 = P[1][1]*t9;
|
|
float t24 = P[8][1]*t104;
|
|
float t25 = P[0][1]*t105;
|
|
float t26 = P[5][1]*t100;
|
|
float t64 = P[4][1]*t101;
|
|
float t65 = P[2][1]*t102;
|
|
float t66 = P[3][1]*t103;
|
|
float t27 = t23+t24+t25+t26-t64-t65-t66;
|
|
float t28 = t9*t27;
|
|
float t29 = P[1][4]*t9;
|
|
float t30 = P[8][4]*t104;
|
|
float t31 = P[0][4]*t105;
|
|
float t32 = P[5][4]*t100;
|
|
float t67 = P[4][4]*t101;
|
|
float t68 = P[2][4]*t102;
|
|
float t69 = P[3][4]*t103;
|
|
float t33 = t29+t30+t31+t32-t67-t68-t69;
|
|
float t34 = P[1][8]*t9;
|
|
float t35 = P[8][8]*t104;
|
|
float t36 = P[0][8]*t105;
|
|
float t37 = P[5][8]*t100;
|
|
float t71 = P[4][8]*t101;
|
|
float t72 = P[2][8]*t102;
|
|
float t73 = P[3][8]*t103;
|
|
float t38 = t34+t35+t36+t37-t71-t72-t73;
|
|
float t39 = t104*t38;
|
|
float t40 = P[1][0]*t9;
|
|
float t41 = P[8][0]*t104;
|
|
float t42 = P[5][0]*t100;
|
|
float t74 = P[4][0]*t101;
|
|
float t75 = P[2][0]*t102;
|
|
float t76 = P[3][0]*t103;
|
|
float t43 = t22+t40+t41+t42-t74-t75-t76;
|
|
float t44 = t105*t43;
|
|
float t45 = P[1][2]*t9;
|
|
float t46 = P[8][2]*t104;
|
|
float t47 = P[0][2]*t105;
|
|
float t48 = P[5][2]*t100;
|
|
float t63 = P[2][2]*t102;
|
|
float t77 = P[4][2]*t101;
|
|
float t78 = P[3][2]*t103;
|
|
float t49 = t45+t46+t47+t48-t63-t77-t78;
|
|
float t50 = P[1][5]*t9;
|
|
float t51 = P[8][5]*t104;
|
|
float t52 = P[0][5]*t105;
|
|
float t53 = P[5][5]*t100;
|
|
float t80 = P[4][5]*t101;
|
|
float t81 = P[2][5]*t102;
|
|
float t82 = P[3][5]*t103;
|
|
float t54 = t50+t51+t52+t53-t80-t81-t82;
|
|
float t55 = t100*t54;
|
|
float t56 = P[1][3]*t9;
|
|
float t57 = P[8][3]*t104;
|
|
float t58 = P[0][3]*t105;
|
|
float t59 = P[5][3]*t100;
|
|
float t83 = P[4][3]*t101;
|
|
float t84 = P[2][3]*t102;
|
|
float t85 = P[3][3]*t103;
|
|
float t60 = t56+t57+t58+t59-t83-t84-t85;
|
|
float t70 = t101*t33;
|
|
float t79 = t102*t49;
|
|
float t86 = t103*t60;
|
|
float t61 = R_LOS+t28+t39+t44+t55-t70-t79-t86;
|
|
float t62 = 1.0f/t61;
|
|
|
|
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
|
|
if (t61 > R_LOS) {
|
|
t62 = 1.0f/t61;
|
|
faultStatus.bad_yflow = false;
|
|
} else {
|
|
t61 = 0.0f;
|
|
t62 = 1.0f/R_LOS;
|
|
faultStatus.bad_yflow = true;
|
|
return;
|
|
}
|
|
varInnovOptFlow[1] = t61;
|
|
|
|
// calculate innovation for Y observation
|
|
innovOptFlow[1] = losPred[1] - ofDataDelayed.flowRadXYcomp.y;
|
|
|
|
// calculate Kalman gains for the Y-axis observation
|
|
Kfusion[0] = -t62*(t22+P[0][1]*t9+P[0][5]*t100-P[0][4]*t101-P[0][2]*t102-P[0][3]*t103+P[0][8]*t104);
|
|
Kfusion[1] = -t62*(t23+P[1][5]*t100+P[1][0]*t105-P[1][4]*t101-P[1][2]*t102-P[1][3]*t103+P[1][8]*t104);
|
|
Kfusion[2] = -t62*(-t63+P[2][1]*t9+P[2][5]*t100+P[2][0]*t105-P[2][4]*t101-P[2][3]*t103+P[2][8]*t104);
|
|
Kfusion[3] = -t62*(-t85+P[3][1]*t9+P[3][5]*t100+P[3][0]*t105-P[3][4]*t101-P[3][2]*t102+P[3][8]*t104);
|
|
Kfusion[4] = -t62*(-t67+P[4][1]*t9+P[4][5]*t100+P[4][0]*t105-P[4][2]*t102-P[4][3]*t103+P[4][8]*t104);
|
|
Kfusion[5] = -t62*(t53+P[5][1]*t9+P[5][0]*t105-P[5][4]*t101-P[5][2]*t102-P[5][3]*t103+P[5][8]*t104);
|
|
Kfusion[6] = -t62*(P[6][1]*t9+P[6][5]*t100+P[6][0]*t105-P[6][4]*t101-P[6][2]*t102-P[6][3]*t103+P[6][8]*t104);
|
|
Kfusion[7] = -t62*(P[7][1]*t9+P[7][5]*t100+P[7][0]*t105-P[7][4]*t101-P[7][2]*t102-P[7][3]*t103+P[7][8]*t104);
|
|
Kfusion[8] = -t62*(t35+P[8][1]*t9+P[8][5]*t100+P[8][0]*t105-P[8][4]*t101-P[8][2]*t102-P[8][3]*t103);
|
|
Kfusion[9] = -t62*(P[9][1]*t9+P[9][5]*t100+P[9][0]*t105-P[9][4]*t101-P[9][2]*t102-P[9][3]*t103+P[9][8]*t104);
|
|
Kfusion[10] = -t62*(P[10][1]*t9+P[10][5]*t100+P[10][0]*t105-P[10][4]*t101-P[10][2]*t102-P[10][3]*t103+P[10][8]*t104);
|
|
Kfusion[11] = -t62*(P[11][1]*t9+P[11][5]*t100+P[11][0]*t105-P[11][4]*t101-P[11][2]*t102-P[11][3]*t103+P[11][8]*t104);
|
|
Kfusion[12] = -t62*(P[12][1]*t9+P[12][5]*t100+P[12][0]*t105-P[12][4]*t101-P[12][2]*t102-P[12][3]*t103+P[12][8]*t104);
|
|
Kfusion[13] = -t62*(P[13][1]*t9+P[13][5]*t100+P[13][0]*t105-P[13][4]*t101-P[13][2]*t102-P[13][3]*t103+P[13][8]*t104);
|
|
Kfusion[14] = -t62*(P[14][1]*t9+P[14][5]*t100+P[14][0]*t105-P[14][4]*t101-P[14][2]*t102-P[14][3]*t103+P[14][8]*t104);
|
|
Kfusion[15] = -t62*(P[15][1]*t9+P[15][5]*t100+P[15][0]*t105-P[15][4]*t101-P[15][2]*t102-P[15][3]*t103+P[15][8]*t104);
|
|
if (!inhibitWindStates) {
|
|
Kfusion[22] = -t62*(P[22][1]*t9+P[22][5]*t100+P[22][0]*t105-P[22][4]*t101-P[22][2]*t102-P[22][3]*t103+P[22][8]*t104);
|
|
Kfusion[23] = -t62*(P[23][1]*t9+P[23][5]*t100+P[23][0]*t105-P[23][4]*t101-P[23][2]*t102-P[23][3]*t103+P[23][8]*t104);
|
|
} else {
|
|
Kfusion[22] = 0.0f;
|
|
Kfusion[23] = 0.0f;
|
|
}
|
|
if (!inhibitMagStates) {
|
|
Kfusion[16] = -t62*(P[16][1]*t9+P[16][5]*t100+P[16][0]*t105-P[16][4]*t101-P[16][2]*t102-P[16][3]*t103+P[16][8]*t104);
|
|
Kfusion[17] = -t62*(P[17][1]*t9+P[17][5]*t100+P[17][0]*t105-P[17][4]*t101-P[17][2]*t102-P[17][3]*t103+P[17][8]*t104);
|
|
Kfusion[18] = -t62*(P[18][1]*t9+P[18][5]*t100+P[18][0]*t105-P[18][4]*t101-P[18][2]*t102-P[18][3]*t103+P[18][8]*t104);
|
|
Kfusion[19] = -t62*(P[19][1]*t9+P[19][5]*t100+P[19][0]*t105-P[19][4]*t101-P[19][2]*t102-P[19][3]*t103+P[19][8]*t104);
|
|
Kfusion[20] = -t62*(P[20][1]*t9+P[20][5]*t100+P[20][0]*t105-P[20][4]*t101-P[20][2]*t102-P[20][3]*t103+P[20][8]*t104);
|
|
Kfusion[21] = -t62*(P[21][1]*t9+P[21][5]*t100+P[21][0]*t105-P[21][4]*t101-P[21][2]*t102-P[21][3]*t103+P[21][8]*t104);
|
|
} else {
|
|
for (uint8_t i = 16; i <= 21; i++) {
|
|
Kfusion[i] = 0.0f;
|
|
}
|
|
}
|
|
}
|
|
|
|
// calculate the innovation consistency test ratio
|
|
flowTestRatio[obsIndex] = sq(innovOptFlow[obsIndex]) / (sq(MAX(0.01f * (float)frontend->_flowInnovGate, 1.0f)) * varInnovOptFlow[obsIndex]);
|
|
|
|
// Check the innovation for consistency and don't fuse if out of bounds or flow is too fast to be reliable
|
|
if ((flowTestRatio[obsIndex]) < 1.0f && (ofDataDelayed.flowRadXY.x < frontend->_maxFlowRate) && (ofDataDelayed.flowRadXY.y < frontend->_maxFlowRate)) {
|
|
// record the last time observations were accepted for fusion
|
|
prevFlowFuseTime_ms = imuSampleTime_ms;
|
|
|
|
// 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<=5; j++) {
|
|
KH[i][j] = Kfusion[i] * H_LOS[j];
|
|
}
|
|
for (unsigned j = 6; j<=7; j++) {
|
|
KH[i][j] = 0.0f;
|
|
}
|
|
KH[i][8] = Kfusion[i] * H_LOS[8];
|
|
for (unsigned j = 9; 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];
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|
res += KH[i][2] * P[2][j];
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|
res += KH[i][3] * P[3][j];
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|
res += KH[i][4] * P[4][j];
|
|
res += KH[i][5] * P[5][j];
|
|
res += KH[i][8] * P[8][j];
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|
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-condiioning.
|
|
ForceSymmetry();
|
|
ConstrainVariances();
|
|
|
|
// zero the attitude error state - by definition it is assumed to be zero before each observaton fusion
|
|
stateStruct.angErr.zero();
|
|
|
|
// correct the state vector
|
|
for (uint8_t j= 0; j<=stateIndexLim; j++) {
|
|
statesArray[j] = statesArray[j] - Kfusion[j] * innovOptFlow[obsIndex];
|
|
}
|
|
|
|
// the first 3 states represent the angular misalignment vector. This is
|
|
// is used to correct the estimated quaternion on the current time step
|
|
stateStruct.quat.rotate(stateStruct.angErr);
|
|
|
|
} else {
|
|
// record bad axis
|
|
if (obsIndex == 0) {
|
|
faultStatus.bad_xflow = true;
|
|
} else if (obsIndex == 1) {
|
|
faultStatus.bad_yflow = true;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
/********************************************************
|
|
* MISC FUNCTIONS *
|
|
********************************************************/
|
|
|
|
#endif // HAL_CPU_CLASS
|