#include <AP_HAL/AP_HAL.h>
#include "AP_NavEKF3.h"
#include "AP_NavEKF3_core.h"
#include <AP_AHRS/AP_AHRS.h>
#include <AP_Vehicle/AP_Vehicle.h>
#include <GCS_MAVLink/GCS.h>
#include <AP_RangeFinder/RangeFinder_Backend.h>
#include <AP_GPS/AP_GPS.h>
#include <AP_Baro/AP_Baro.h>
extern const AP_HAL::HAL& hal;
/********************************************************
* RESET FUNCTIONS *
********************************************************/
// Reset velocity states to last GPS measurement if available or to zero if in constant position mode or if PV aiding is not absolute
// Do not reset vertical velocity using GPS as there is baro alt available to constrain drift
void NavEKF3_core::ResetVelocity(void)
{
// Store the position before the reset so that we can record the reset delta
velResetNE.x = stateStruct.velocity.x;
velResetNE.y = stateStruct.velocity.y;
// reset the corresponding covariances
zeroRows(P,4,5);
zeroCols(P,4,5);
if (PV_AidingMode != AID_ABSOLUTE) {
stateStruct.velocity.zero();
// set the variances using the measurement noise parameter
P[5][5] = P[4][4] = sq(frontend->_gpsHorizVelNoise);
} else {
// reset horizontal velocity states to the GPS velocity if available
if ((imuSampleTime_ms - lastTimeGpsReceived_ms < 250 && velResetSource == DEFAULT) || velResetSource == GPS) {
stateStruct.velocity.x = gpsDataNew.vel.x;
stateStruct.velocity.y = gpsDataNew.vel.y;
// set the variances using the reported GPS speed accuracy
P[5][5] = P[4][4] = sq(MAX(frontend->_gpsHorizVelNoise,gpsSpdAccuracy));
// clear the timeout flags and counters
velTimeout = false;
lastVelPassTime_ms = imuSampleTime_ms;
} else {
stateStruct.velocity.x = 0.0f;
stateStruct.velocity.y = 0.0f;
// set the variances using the likely speed range
P[5][5] = P[4][4] = sq(25.0f);
// clear the timeout flags and counters
velTimeout = false;
lastVelPassTime_ms = imuSampleTime_ms;
}
}
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].velocity.x = stateStruct.velocity.x;
storedOutput[i].velocity.y = stateStruct.velocity.y;
}
outputDataNew.velocity.x = stateStruct.velocity.x;
outputDataNew.velocity.y = stateStruct.velocity.y;
outputDataDelayed.velocity.x = stateStruct.velocity.x;
outputDataDelayed.velocity.y = stateStruct.velocity.y;
// Calculate the position jump due to the reset
velResetNE.x = stateStruct.velocity.x - velResetNE.x;
velResetNE.y = stateStruct.velocity.y - velResetNE.y;
// store the time of the reset
lastVelReset_ms = imuSampleTime_ms;
// clear reset data source preference
velResetSource = DEFAULT;
}
// resets position states to last GPS measurement or to zero if in constant position mode
void NavEKF3_core::ResetPosition(void)
{
// Store the position before the reset so that we can record the reset delta
posResetNE.x = stateStruct.position.x;
posResetNE.y = stateStruct.position.y;
// reset the corresponding covariances
zeroRows(P,7,8);
zeroCols(P,7,8);
if (PV_AidingMode != AID_ABSOLUTE) {
// reset all position state history to the last known position
stateStruct.position.x = lastKnownPositionNE.x;
stateStruct.position.y = lastKnownPositionNE.y;
// set the variances using the position measurement noise parameter
P[7][7] = P[8][8] = sq(frontend->_gpsHorizPosNoise);
} else {
// Use GPS data as first preference if fresh data is available
if ((imuSampleTime_ms - lastTimeGpsReceived_ms < 250 && posResetSource == DEFAULT) || posResetSource == GPS) {
// record the ID of the GPS for the data we are using for the reset
last_gps_idx = gpsDataNew.sensor_idx;
// write to state vector and compensate for offset between last GPS measurement and the EKF time horizon
stateStruct.position.x = gpsDataNew.pos.x + 0.001f*gpsDataNew.vel.x*(float(imuDataDelayed.time_ms) - float(gpsDataNew.time_ms));
stateStruct.position.y = gpsDataNew.pos.y + 0.001f*gpsDataNew.vel.y*(float(imuDataDelayed.time_ms) - float(gpsDataNew.time_ms));
// set the variances using the position measurement noise parameter
P[7][7] = P[8][8] = sq(MAX(gpsPosAccuracy,frontend->_gpsHorizPosNoise));
// clear the timeout flags and counters
posTimeout = false;
lastPosPassTime_ms = imuSampleTime_ms;
} else if ((imuSampleTime_ms - rngBcnLast3DmeasTime_ms < 250 && posResetSource == DEFAULT) || posResetSource == RNGBCN) {
// use the range beacon data as a second preference
stateStruct.position.x = receiverPos.x;
stateStruct.position.y = receiverPos.y;
// set the variances from the beacon alignment filter
P[7][7] = receiverPosCov[0][0];
P[8][8] = receiverPosCov[1][1];
// clear the timeout flags and counters
rngBcnTimeout = false;
lastRngBcnPassTime_ms = imuSampleTime_ms;
}
}
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].position.x = stateStruct.position.x;
storedOutput[i].position.y = stateStruct.position.y;
}
outputDataNew.position.x = stateStruct.position.x;
outputDataNew.position.y = stateStruct.position.y;
outputDataDelayed.position.x = stateStruct.position.x;
outputDataDelayed.position.y = stateStruct.position.y;
// Calculate the position jump due to the reset
posResetNE.x = stateStruct.position.x - posResetNE.x;
posResetNE.y = stateStruct.position.y - posResetNE.y;
// store the time of the reset
lastPosReset_ms = imuSampleTime_ms;
// clear reset source preference
posResetSource = DEFAULT;
}
// reset the vertical position state using the last height measurement
void NavEKF3_core::ResetHeight(void)
{
// Store the position before the reset so that we can record the reset delta
posResetD = stateStruct.position.z;
// write to the state vector
stateStruct.position.z = -hgtMea;
outputDataNew.position.z = stateStruct.position.z;
outputDataDelayed.position.z = stateStruct.position.z;
// reset the terrain state height
if (onGround) {
// assume vehicle is sitting on the ground
terrainState = stateStruct.position.z + rngOnGnd;
} else {
// can make no assumption other than vehicle is not below ground level
terrainState = MAX(stateStruct.position.z + rngOnGnd , terrainState);
}
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].position.z = stateStruct.position.z;
}
// Calculate the position jump due to the reset
posResetD = stateStruct.position.z - posResetD;
// store the time of the reset
lastPosResetD_ms = imuSampleTime_ms;
// clear the timeout flags and counters
hgtTimeout = false;
lastHgtPassTime_ms = imuSampleTime_ms;
// reset the corresponding covariances
zeroRows(P,9,9);
zeroCols(P,9,9);
// set the variances to the measurement variance
P[9][9] = posDownObsNoise;
// Reset the vertical velocity state using GPS vertical velocity if we are airborne
// Check that GPS vertical velocity data is available and can be used
if (inFlight && !gpsNotAvailable && frontend->_fusionModeGPS == 0 && !frontend->inhibitGpsVertVelUse) {
stateStruct.velocity.z = gpsDataNew.vel.z;
} else if (onGround) {
stateStruct.velocity.z = 0.0f;
}
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].velocity.z = stateStruct.velocity.z;
}
outputDataNew.velocity.z = stateStruct.velocity.z;
outputDataDelayed.velocity.z = stateStruct.velocity.z;
// reset the corresponding covariances
zeroRows(P,6,6);
zeroCols(P,6,6);
// set the variances to the measurement variance
P[6][6] = sq(frontend->_gpsVertVelNoise);
}
// Zero the EKF height datum
// Return true if the height datum reset has been performed
bool NavEKF3_core::resetHeightDatum(void)
{
if (activeHgtSource == HGT_SOURCE_RNG || !onGround) {
// only allow resets when on the ground.
// If using using rangefinder for height then never perform a
// reset of the height datum
return false;
}
// record the old height estimate
float oldHgt = -stateStruct.position.z;
// reset the barometer so that it reads zero at the current height
AP::baro().update_calibration();
// reset the height state
stateStruct.position.z = 0.0f;
// adjust the height of the EKF origin so that the origin plus baro height before and after the reset is the same
if (validOrigin) {
if (!gpsGoodToAlign) {
// if we don't have GPS lock then we shouldn't be doing a
// resetHeightDatum, but if we do then the best option is
// to maintain the old error
ekfGpsRefHgt += (int32_t)(100.0f * oldHgt);
} else {
// if we have a good GPS lock then reset to the GPS
// altitude. This ensures the reported AMSL alt from
// getLLH() is equal to GPS altitude, while also ensuring
// that the relative alt is zero
ekfGpsRefHgt = AP::gps().location().alt*0.01;
}
}
// set the terrain state to zero (on ground). The adjustment for
// frame height will get added in the later constraints
terrainState = 0;
return true;
}
/********************************************************
* FUSE MEASURED_DATA *
********************************************************/
// select fusion of velocity, position and height measurements
void NavEKF3_core::SelectVelPosFusion()
{
// Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz
// If so, don't fuse measurements on this time step to reduce frame over-runs
// Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements
if (magFusePerformed && dtIMUavg < 0.005f && !posVelFusionDelayed) {
posVelFusionDelayed = true;
return;
} else {
posVelFusionDelayed = false;
}
// read GPS data from the sensor and check for new data in the buffer
readGpsData();
gpsDataToFuse = storedGPS.recall(gpsDataDelayed,imuDataDelayed.time_ms);
// Determine if we need to fuse position and velocity data on this time step
if (gpsDataToFuse && PV_AidingMode == AID_ABSOLUTE) {
// correct GPS data for position offset of antenna phase centre relative to the IMU
Vector3f posOffsetBody = AP::gps().get_antenna_offset(gpsDataDelayed.sensor_idx) - accelPosOffset;
if (!posOffsetBody.is_zero()) {
if (fuseVelData) {
// TODO use a filtered angular rate with a group delay that matches the GPS delay
Vector3f angRate = imuDataDelayed.delAng * (1.0f/imuDataDelayed.delAngDT);
Vector3f velOffsetBody = angRate % posOffsetBody;
Vector3f velOffsetEarth = prevTnb.mul_transpose(velOffsetBody);
gpsDataDelayed.vel -= velOffsetEarth;
}
Vector3f posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
gpsDataDelayed.pos.x -= posOffsetEarth.x;
gpsDataDelayed.pos.y -= posOffsetEarth.y;
gpsDataDelayed.hgt += posOffsetEarth.z;
}
// Don't fuse velocity data if GPS doesn't support it
if (frontend->_fusionModeGPS <= 1) {
fuseVelData = true;
} else {
fuseVelData = false;
}
fusePosData = true;
} else {
fuseVelData = false;
fusePosData = false;
}
// we have GPS data to fuse and a request to align the yaw using the GPS course
if (gpsYawResetRequest) {
realignYawGPS();
}
// Select height data to be fused from the available baro, range finder and GPS sources
selectHeightForFusion();
// if we are using GPS, check for a change in receiver and reset position and height
if (gpsDataToFuse && PV_AidingMode == AID_ABSOLUTE && gpsDataDelayed.sensor_idx != last_gps_idx) {
// record the ID of the GPS that we are using for the reset
last_gps_idx = gpsDataDelayed.sensor_idx;
// Store the position before the reset so that we can record the reset delta
posResetNE.x = stateStruct.position.x;
posResetNE.y = stateStruct.position.y;
// Set the position states to the position from the new GPS
stateStruct.position.x = gpsDataNew.pos.x;
stateStruct.position.y = gpsDataNew.pos.y;
// Calculate the position offset due to the reset
posResetNE.x = stateStruct.position.x - posResetNE.x;
posResetNE.y = stateStruct.position.y - posResetNE.y;
// Add the offset to the output observer states
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].position.x += posResetNE.x;
storedOutput[i].position.y += posResetNE.y;
}
outputDataNew.position.x += posResetNE.x;
outputDataNew.position.y += posResetNE.y;
outputDataDelayed.position.x += posResetNE.x;
outputDataDelayed.position.y += posResetNE.y;
// store the time of the reset
lastPosReset_ms = imuSampleTime_ms;
// If we are alseo using GPS as the height reference, reset the height
if (activeHgtSource == HGT_SOURCE_GPS) {
// Store the position before the reset so that we can record the reset delta
posResetD = stateStruct.position.z;
// write to the state vector
stateStruct.position.z = -hgtMea;
// Calculate the position jump due to the reset
posResetD = stateStruct.position.z - posResetD;
// Add the offset to the output observer states
outputDataNew.position.z += posResetD;
outputDataDelayed.position.z += posResetD;
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].position.z += posResetD;
}
// store the time of the reset
lastPosResetD_ms = imuSampleTime_ms;
}
}
// If we are operating without any aiding, fuse in the last known position
// to constrain tilt drift. This assumes a non-manoeuvring vehicle
// Do this to coincide with the height fusion
if (fuseHgtData && PV_AidingMode == AID_NONE) {
gpsDataDelayed.vel.zero();
gpsDataDelayed.pos.x = lastKnownPositionNE.x;
gpsDataDelayed.pos.y = lastKnownPositionNE.y;
fusePosData = true;
fuseVelData = false;
}
// perform fusion
if (fuseVelData || fusePosData || fuseHgtData) {
FuseVelPosNED();
// clear the flags to prevent repeated fusion of the same data
fuseVelData = false;
fuseHgtData = false;
fusePosData = false;
}
}
// fuse selected position, velocity and height measurements
void NavEKF3_core::FuseVelPosNED()
{
// start performance timer
hal.util->perf_begin(_perf_FuseVelPosNED);
// health is set bad until test passed
velHealth = false;
posHealth = false;
hgtHealth = false;
// declare variables used to check measurement errors
Vector3f velInnov;
// declare variables used to control access to arrays
bool fuseData[6] = {false,false,false,false,false,false};
uint8_t stateIndex;
uint8_t obsIndex;
// declare variables used by state and covariance update calculations
Vector6 R_OBS; // Measurement variances used for fusion
Vector6 R_OBS_DATA_CHECKS; // Measurement variances used for data checks only
Vector6 observation;
float SK;
// perform sequential fusion of GPS measurements. This assumes that the
// errors in the different velocity and position components are
// uncorrelated which is not true, however in the absence of covariance
// data from the GPS receiver it is the only assumption we can make
// so we might as well take advantage of the computational efficiencies
// associated with sequential fusion
if (fuseVelData || fusePosData || fuseHgtData) {
// form the observation vector
observation[0] = gpsDataDelayed.vel.x;
observation[1] = gpsDataDelayed.vel.y;
observation[2] = gpsDataDelayed.vel.z;
observation[3] = gpsDataDelayed.pos.x;
observation[4] = gpsDataDelayed.pos.y;
observation[5] = -hgtMea;
// calculate additional error in GPS position caused by manoeuvring
float posErr = frontend->gpsPosVarAccScale * accNavMag;
// estimate the GPS Velocity, GPS horiz position and height measurement variances.
// Use different errors if operating without external aiding using an assumed position or velocity of zero
if (PV_AidingMode == AID_NONE) {
if (tiltAlignComplete && motorsArmed) {
// This is a compromise between corrections for gyro errors and reducing effect of manoeuvre accelerations on tilt estimate
R_OBS[0] = sq(constrain_float(frontend->_noaidHorizNoise, 0.5f, 50.0f));
} else {
// Use a smaller value to give faster initial alignment
R_OBS[0] = sq(0.5f);
}
R_OBS[1] = R_OBS[0];
R_OBS[2] = R_OBS[0];
R_OBS[3] = R_OBS[0];
R_OBS[4] = R_OBS[0];
for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
} else {
if (gpsSpdAccuracy > 0.0f) {
// use GPS receivers reported speed accuracy if available and floor at value set by GPS velocity noise parameter
R_OBS[0] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsHorizVelNoise, 50.0f));
R_OBS[2] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsVertVelNoise, 50.0f));
} else {
// calculate additional error in GPS velocity caused by manoeuvring
R_OBS[0] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
R_OBS[2] = sq(constrain_float(frontend->_gpsVertVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsDVelVarAccScale * accNavMag);
}
R_OBS[1] = R_OBS[0];
// Use GPS reported position accuracy if available and floor at value set by GPS position noise parameter
if (gpsPosAccuracy > 0.0f) {
R_OBS[3] = sq(constrain_float(gpsPosAccuracy, frontend->_gpsHorizPosNoise, 100.0f));
} else {
R_OBS[3] = sq(constrain_float(frontend->_gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
}
R_OBS[4] = R_OBS[3];
// For data integrity checks we use the same measurement variances as used to calculate the Kalman gains for all measurements except GPS horizontal velocity
// 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 performance
// plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
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);
}
R_OBS[5] = posDownObsNoise;
for (uint8_t i=3; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
// if vertical GPS velocity data and an independent height source is being used, check to see if the GPS vertical velocity and altimeter
// innovations have the same sign and are outside limits. If so, then it is likely aliasing is affecting
// the accelerometers and we should disable the GPS and barometer innovation consistency checks.
if (useGpsVertVel && fuseVelData && (frontend->_altSource != 2)) {
// calculate innovations for height and vertical GPS vel measurements
float hgtErr = stateStruct.position.z - observation[5];
float velDErr = stateStruct.velocity.z - observation[2];
// check if they are the same sign and both more than 3-sigma out of bounds
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]))) {
badIMUdata = true;
} else {
badIMUdata = false;
}
}
// calculate innovations and check GPS data validity using an innovation consistency check
// test position measurements
if (fusePosData) {
// test horizontal position measurements
innovVelPos[3] = stateStruct.position.x - observation[3];
innovVelPos[4] = stateStruct.position.y - observation[4];
varInnovVelPos[3] = P[7][7] + R_OBS_DATA_CHECKS[3];
varInnovVelPos[4] = P[8][8] + R_OBS_DATA_CHECKS[4];
// apply an innovation consistency threshold test, but don't fail if bad IMU data
float maxPosInnov2 = sq(MAX(0.01f * (float)frontend->_gpsPosInnovGate, 1.0f))*(varInnovVelPos[3] + varInnovVelPos[4]);
posTestRatio = (sq(innovVelPos[3]) + sq(innovVelPos[4])) / maxPosInnov2;
posHealth = ((posTestRatio < 1.0f) || badIMUdata);
// use position data if healthy or timed out
if (PV_AidingMode == AID_NONE) {
posHealth = true;
lastPosPassTime_ms = imuSampleTime_ms;
} else if (posHealth || posTimeout) {
posHealth = true;
lastPosPassTime_ms = imuSampleTime_ms;
// if timed out or outside the specified uncertainty radius, reset to the GPS
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 > 0 || PV_AidingMode != AID_ABSOLUTE || frontend->inhibitGpsVertVelUse) {
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() && activeHgtSource == HGT_SOURCE_BARO) {
// 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<=9; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
// inhibit delta angle bias state estmation by setting Kalman gains to zero
if (!inhibitDelAngBiasStates) {
for (uint8_t i = 10; i<=12; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
} else {
// zero indexes 10 to 12 = 3*4 bytes
memset(&Kfusion[10], 0, 12);
}
// inhibit delta velocity bias state estimation by setting Kalman gains to zero
if (!inhibitDelVelBiasStates) {
for (uint8_t i = 13; i<=15; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
} else {
// zero indexes 13 to 15 = 3*4 bytes
memset(&Kfusion[13], 0, 12);
}
// 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 {
// zero indexes 16 to 21 = 6*4 bytes
memset(&Kfusion[16], 0, 24);
}
// 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 {
// zero indexes 22 to 23 = 2*4 bytes
memset(&Kfusion[22], 0, 8);
}
// 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-conditioning.
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) {
AP_RangeFinder_Backend *sensor = frontend->_rng.get_backend(rangeDataDelayed.sensor_idx);
if (sensor != nullptr) {
Vector3f posOffsetBody = sensor->get_pos_offset() - 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_orient(ROTATION_PITCH_270) * (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;
}
}
// If we are not using GPS as the primary height sensor, correct EKF origin height so that
// combined local NED position height and origin height remains consistent with the GPS altitude
// This also enables the GPS height to be used as a backup height source
if (gpsDataToFuse &&
(((frontend->_originHgtMode & (1 << 0)) && (activeHgtSource == HGT_SOURCE_BARO)) ||
((frontend->_originHgtMode & (1 << 1)) && (activeHgtSource == HGT_SOURCE_RNG)))
) {
correctEkfOriginHeight();
}
// 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;
}
}
/*
* Fuse body frame velocity measurements using explicit algebraic equations generated with Matlab symbolic toolbox.
* The script file used to generate these and other equations in this filter can be found here:
* https://github.com/PX4/ecl/blob/master/matlab/scripts/Inertial%20Nav%20EKF/GenerateNavFilterEquations.m
*/
void NavEKF3_core::FuseBodyVel()
{
Vector24 H_VEL;
Vector3f bodyVelPred;
// Copy required states to local variable names
float q0 = stateStruct.quat[0];
float q1 = stateStruct.quat[1];
float q2 = stateStruct.quat[2];
float q3 = stateStruct.quat[3];
float vn = stateStruct.velocity.x;
float ve = stateStruct.velocity.y;
float vd = stateStruct.velocity.z;
// Fuse X, Y and Z axis measurements sequentially assuming observation errors are uncorrelated
for (uint8_t obsIndex=0; obsIndex<=2; obsIndex++) {
// calculate relative velocity in sensor frame including the relative motion due to rotation
bodyVelPred = (prevTnb * stateStruct.velocity);
// correct sensor offset body frame position offset relative to IMU
Vector3f posOffsetBody = (*bodyOdmDataDelayed.body_offset) - accelPosOffset;
// correct prediction for relative motion due to rotation
// note - % operator overloaded for cross product
if (imuDataDelayed.delAngDT > 0.001f) {
bodyVelPred += (imuDataDelayed.delAng * (1.0f / imuDataDelayed.delAngDT)) % posOffsetBody;
}
// calculate observation jacobians and Kalman gains
if (obsIndex == 0) {
// calculate X axis observation Jacobian
H_VEL[0] = q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f;
H_VEL[1] = q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f;
H_VEL[2] = q0*vd*-2.0f+q1*ve*2.0f-q2*vn*2.0f;
H_VEL[3] = q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f;
H_VEL[4] = q0*q0+q1*q1-q2*q2-q3*q3;
H_VEL[5] = q0*q3*2.0f+q1*q2*2.0f;
H_VEL[6] = q0*q2*-2.0f+q1*q3*2.0f;
for (uint8_t index = 7; index < 24; index++) {
H_VEL[index] = 0.0f;
}
// calculate intermediate expressions for X axis Kalman gains
float R_VEL = sq(bodyOdmDataDelayed.velErr);
float t2 = q0*q3*2.0f;
float t3 = q1*q2*2.0f;
float t4 = t2+t3;
float t5 = q0*q0;
float t6 = q1*q1;
float t7 = q2*q2;
float t8 = q3*q3;
float t9 = t5+t6-t7-t8;
float t10 = q0*q2*2.0f;
float t25 = q1*q3*2.0f;
float t11 = t10-t25;
float t12 = q3*ve*2.0f;
float t13 = q0*vn*2.0f;
float t26 = q2*vd*2.0f;
float t14 = t12+t13-t26;
float t15 = q3*vd*2.0f;
float t16 = q2*ve*2.0f;
float t17 = q1*vn*2.0f;
float t18 = t15+t16+t17;
float t19 = q0*vd*2.0f;
float t20 = q2*vn*2.0f;
float t27 = q1*ve*2.0f;
float t21 = t19+t20-t27;
float t22 = q1*vd*2.0f;
float t23 = q0*ve*2.0f;
float t28 = q3*vn*2.0f;
float t24 = t22+t23-t28;
float t29 = P[0][0]*t14;
float t30 = P[1][1]*t18;
float t31 = P[4][5]*t9;
float t32 = P[5][5]*t4;
float t33 = P[0][5]*t14;
float t34 = P[1][5]*t18;
float t35 = P[3][5]*t24;
float t79 = P[6][5]*t11;
float t80 = P[2][5]*t21;
float t36 = t31+t32+t33+t34+t35-t79-t80;
float t37 = t4*t36;
float t38 = P[4][6]*t9;
float t39 = P[5][6]*t4;
float t40 = P[0][6]*t14;
float t41 = P[1][6]*t18;
float t42 = P[3][6]*t24;
float t81 = P[6][6]*t11;
float t82 = P[2][6]*t21;
float t43 = t38+t39+t40+t41+t42-t81-t82;
float t44 = P[4][0]*t9;
float t45 = P[5][0]*t4;
float t46 = P[1][0]*t18;
float t47 = P[3][0]*t24;
float t84 = P[6][0]*t11;
float t85 = P[2][0]*t21;
float t48 = t29+t44+t45+t46+t47-t84-t85;
float t49 = t14*t48;
float t50 = P[4][1]*t9;
float t51 = P[5][1]*t4;
float t52 = P[0][1]*t14;
float t53 = P[3][1]*t24;
float t86 = P[6][1]*t11;
float t87 = P[2][1]*t21;
float t54 = t30+t50+t51+t52+t53-t86-t87;
float t55 = t18*t54;
float t56 = P[4][2]*t9;
float t57 = P[5][2]*t4;
float t58 = P[0][2]*t14;
float t59 = P[1][2]*t18;
float t60 = P[3][2]*t24;
float t78 = P[2][2]*t21;
float t88 = P[6][2]*t11;
float t61 = t56+t57+t58+t59+t60-t78-t88;
float t62 = P[4][3]*t9;
float t63 = P[5][3]*t4;
float t64 = P[0][3]*t14;
float t65 = P[1][3]*t18;
float t66 = P[3][3]*t24;
float t90 = P[6][3]*t11;
float t91 = P[2][3]*t21;
float t67 = t62+t63+t64+t65+t66-t90-t91;
float t68 = t24*t67;
float t69 = P[4][4]*t9;
float t70 = P[5][4]*t4;
float t71 = P[0][4]*t14;
float t72 = P[1][4]*t18;
float t73 = P[3][4]*t24;
float t92 = P[6][4]*t11;
float t93 = P[2][4]*t21;
float t74 = t69+t70+t71+t72+t73-t92-t93;
float t75 = t9*t74;
float t83 = t11*t43;
float t89 = t21*t61;
float t76 = R_VEL+t37+t49+t55+t68+t75-t83-t89;
float t77;
// calculate innovation variance for X axis observation and protect against a badly conditioned calculation
if (t76 > R_VEL) {
t77 = 1.0f/t76;
faultStatus.bad_xvel = false;
} else {
t76 = R_VEL;
t77 = 1.0f/R_VEL;
faultStatus.bad_xvel = true;
return;
}
varInnovBodyVel[0] = t77;
// calculate innovation for X axis observation
innovBodyVel[0] = bodyVelPred.x - bodyOdmDataDelayed.vel.x;
// calculate Kalman gains for X-axis observation
Kfusion[0] = t77*(t29+P[0][5]*t4+P[0][4]*t9-P[0][6]*t11+P[0][1]*t18-P[0][2]*t21+P[0][3]*t24);
Kfusion[1] = t77*(t30+P[1][5]*t4+P[1][4]*t9+P[1][0]*t14-P[1][6]*t11-P[1][2]*t21+P[1][3]*t24);
Kfusion[2] = t77*(-t78+P[2][5]*t4+P[2][4]*t9+P[2][0]*t14-P[2][6]*t11+P[2][1]*t18+P[2][3]*t24);
Kfusion[3] = t77*(t66+P[3][5]*t4+P[3][4]*t9+P[3][0]*t14-P[3][6]*t11+P[3][1]*t18-P[3][2]*t21);
Kfusion[4] = t77*(t69+P[4][5]*t4+P[4][0]*t14-P[4][6]*t11+P[4][1]*t18-P[4][2]*t21+P[4][3]*t24);
Kfusion[5] = t77*(t32+P[5][4]*t9+P[5][0]*t14-P[5][6]*t11+P[5][1]*t18-P[5][2]*t21+P[5][3]*t24);
Kfusion[6] = t77*(-t81+P[6][5]*t4+P[6][4]*t9+P[6][0]*t14+P[6][1]*t18-P[6][2]*t21+P[6][3]*t24);
Kfusion[7] = t77*(P[7][5]*t4+P[7][4]*t9+P[7][0]*t14-P[7][6]*t11+P[7][1]*t18-P[7][2]*t21+P[7][3]*t24);
Kfusion[8] = t77*(P[8][5]*t4+P[8][4]*t9+P[8][0]*t14-P[8][6]*t11+P[8][1]*t18-P[8][2]*t21+P[8][3]*t24);
Kfusion[9] = t77*(P[9][5]*t4+P[9][4]*t9+P[9][0]*t14-P[9][6]*t11+P[9][1]*t18-P[9][2]*t21+P[9][3]*t24);
if (!inhibitDelAngBiasStates) {
Kfusion[10] = t77*(P[10][5]*t4+P[10][4]*t9+P[10][0]*t14-P[10][6]*t11+P[10][1]*t18-P[10][2]*t21+P[10][3]*t24);
Kfusion[11] = t77*(P[11][5]*t4+P[11][4]*t9+P[11][0]*t14-P[11][6]*t11+P[11][1]*t18-P[11][2]*t21+P[11][3]*t24);
Kfusion[12] = t77*(P[12][5]*t4+P[12][4]*t9+P[12][0]*t14-P[12][6]*t11+P[12][1]*t18-P[12][2]*t21+P[12][3]*t24);
} else {
// zero indexes 10 to 12 = 3*4 bytes
memset(&Kfusion[10], 0, 12);
}
if (!inhibitDelVelBiasStates) {
Kfusion[13] = t77*(P[13][5]*t4+P[13][4]*t9+P[13][0]*t14-P[13][6]*t11+P[13][1]*t18-P[13][2]*t21+P[13][3]*t24);
Kfusion[14] = t77*(P[14][5]*t4+P[14][4]*t9+P[14][0]*t14-P[14][6]*t11+P[14][1]*t18-P[14][2]*t21+P[14][3]*t24);
Kfusion[15] = t77*(P[15][5]*t4+P[15][4]*t9+P[15][0]*t14-P[15][6]*t11+P[15][1]*t18-P[15][2]*t21+P[15][3]*t24);
} else {
// zero indexes 13 to 15 = 3*4 bytes
memset(&Kfusion[13], 0, 12);
}
if (!inhibitMagStates) {
Kfusion[16] = t77*(P[16][5]*t4+P[16][4]*t9+P[16][0]*t14-P[16][6]*t11+P[16][1]*t18-P[16][2]*t21+P[16][3]*t24);
Kfusion[17] = t77*(P[17][5]*t4+P[17][4]*t9+P[17][0]*t14-P[17][6]*t11+P[17][1]*t18-P[17][2]*t21+P[17][3]*t24);
Kfusion[18] = t77*(P[18][5]*t4+P[18][4]*t9+P[18][0]*t14-P[18][6]*t11+P[18][1]*t18-P[18][2]*t21+P[18][3]*t24);
Kfusion[19] = t77*(P[19][5]*t4+P[19][4]*t9+P[19][0]*t14-P[19][6]*t11+P[19][1]*t18-P[19][2]*t21+P[19][3]*t24);
Kfusion[20] = t77*(P[20][5]*t4+P[20][4]*t9+P[20][0]*t14-P[20][6]*t11+P[20][1]*t18-P[20][2]*t21+P[20][3]*t24);
Kfusion[21] = t77*(P[21][5]*t4+P[21][4]*t9+P[21][0]*t14-P[21][6]*t11+P[21][1]*t18-P[21][2]*t21+P[21][3]*t24);
} else {
// zero indexes 16 to 21 = 6*4 bytes
memset(&Kfusion[16], 0, 24);
}
if (!inhibitWindStates) {
Kfusion[22] = t77*(P[22][5]*t4+P[22][4]*t9+P[22][0]*t14-P[22][6]*t11+P[22][1]*t18-P[22][2]*t21+P[22][3]*t24);
Kfusion[23] = t77*(P[23][5]*t4+P[23][4]*t9+P[23][0]*t14-P[23][6]*t11+P[23][1]*t18-P[23][2]*t21+P[23][3]*t24);
} else {
// zero indexes 22 to 23 = 2*4 bytes
memset(&Kfusion[22], 0, 8);
}
} else if (obsIndex == 1) {
// calculate Y axis observation Jacobian
H_VEL[0] = q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f;
H_VEL[1] = q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f;
H_VEL[2] = q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f;
H_VEL[3] = q2*vd*2.0f-q3*ve*2.0f-q0*vn*2.0f;
H_VEL[4] = q0*q3*-2.0f+q1*q2*2.0f;
H_VEL[5] = q0*q0-q1*q1+q2*q2-q3*q3;
H_VEL[6] = q0*q1*2.0f+q2*q3*2.0f;
for (uint8_t index = 7; index < 24; index++) {
H_VEL[index] = 0.0f;
}
// calculate intermediate expressions for Y axis Kalman gains
float R_VEL = sq(bodyOdmDataDelayed.velErr);
float t2 = q0*q3*2.0f;
float t9 = q1*q2*2.0f;
float t3 = t2-t9;
float t4 = q0*q0;
float t5 = q1*q1;
float t6 = q2*q2;
float t7 = q3*q3;
float t8 = t4-t5+t6-t7;
float t10 = q0*q1*2.0f;
float t11 = q2*q3*2.0f;
float t12 = t10+t11;
float t13 = q1*vd*2.0f;
float t14 = q0*ve*2.0f;
float t26 = q3*vn*2.0f;
float t15 = t13+t14-t26;
float t16 = q0*vd*2.0f;
float t17 = q2*vn*2.0f;
float t27 = q1*ve*2.0f;
float t18 = t16+t17-t27;
float t19 = q3*vd*2.0f;
float t20 = q2*ve*2.0f;
float t21 = q1*vn*2.0f;
float t22 = t19+t20+t21;
float t23 = q3*ve*2.0f;
float t24 = q0*vn*2.0f;
float t28 = q2*vd*2.0f;
float t25 = t23+t24-t28;
float t29 = P[0][0]*t15;
float t30 = P[1][1]*t18;
float t31 = P[5][4]*t8;
float t32 = P[6][4]*t12;
float t33 = P[0][4]*t15;
float t34 = P[1][4]*t18;
float t35 = P[2][4]*t22;
float t78 = P[4][4]*t3;
float t79 = P[3][4]*t25;
float t36 = t31+t32+t33+t34+t35-t78-t79;
float t37 = P[5][6]*t8;
float t38 = P[6][6]*t12;
float t39 = P[0][6]*t15;
float t40 = P[1][6]*t18;
float t41 = P[2][6]*t22;
float t81 = P[4][6]*t3;
float t82 = P[3][6]*t25;
float t42 = t37+t38+t39+t40+t41-t81-t82;
float t43 = t12*t42;
float t44 = P[5][0]*t8;
float t45 = P[6][0]*t12;
float t46 = P[1][0]*t18;
float t47 = P[2][0]*t22;
float t83 = P[4][0]*t3;
float t84 = P[3][0]*t25;
float t48 = t29+t44+t45+t46+t47-t83-t84;
float t49 = t15*t48;
float t50 = P[5][1]*t8;
float t51 = P[6][1]*t12;
float t52 = P[0][1]*t15;
float t53 = P[2][1]*t22;
float t85 = P[4][1]*t3;
float t86 = P[3][1]*t25;
float t54 = t30+t50+t51+t52+t53-t85-t86;
float t55 = t18*t54;
float t56 = P[5][2]*t8;
float t57 = P[6][2]*t12;
float t58 = P[0][2]*t15;
float t59 = P[1][2]*t18;
float t60 = P[2][2]*t22;
float t87 = P[4][2]*t3;
float t88 = P[3][2]*t25;
float t61 = t56+t57+t58+t59+t60-t87-t88;
float t62 = t22*t61;
float t63 = P[5][3]*t8;
float t64 = P[6][3]*t12;
float t65 = P[0][3]*t15;
float t66 = P[1][3]*t18;
float t67 = P[2][3]*t22;
float t89 = P[4][3]*t3;
float t90 = P[3][3]*t25;
float t68 = t63+t64+t65+t66+t67-t89-t90;
float t69 = P[5][5]*t8;
float t70 = P[6][5]*t12;
float t71 = P[0][5]*t15;
float t72 = P[1][5]*t18;
float t73 = P[2][5]*t22;
float t92 = P[4][5]*t3;
float t93 = P[3][5]*t25;
float t74 = t69+t70+t71+t72+t73-t92-t93;
float t75 = t8*t74;
float t80 = t3*t36;
float t91 = t25*t68;
float t76 = R_VEL+t43+t49+t55+t62+t75-t80-t91;
float t77;
// calculate innovation variance for Y axis observation and protect against a badly conditioned calculation
if (t76 > R_VEL) {
t77 = 1.0f/t76;
faultStatus.bad_yvel = false;
} else {
t76 = R_VEL;
t77 = 1.0f/R_VEL;
faultStatus.bad_yvel = true;
return;
}
varInnovBodyVel[1] = t77;
// calculate innovation for Y axis observation
innovBodyVel[1] = bodyVelPred.y - bodyOdmDataDelayed.vel.y;
// calculate Kalman gains for Y-axis observation
Kfusion[0] = t77*(t29-P[0][4]*t3+P[0][5]*t8+P[0][6]*t12+P[0][1]*t18+P[0][2]*t22-P[0][3]*t25);
Kfusion[1] = t77*(t30-P[1][4]*t3+P[1][5]*t8+P[1][0]*t15+P[1][6]*t12+P[1][2]*t22-P[1][3]*t25);
Kfusion[2] = t77*(t60-P[2][4]*t3+P[2][5]*t8+P[2][0]*t15+P[2][6]*t12+P[2][1]*t18-P[2][3]*t25);
Kfusion[3] = t77*(-t90-P[3][4]*t3+P[3][5]*t8+P[3][0]*t15+P[3][6]*t12+P[3][1]*t18+P[3][2]*t22);
Kfusion[4] = t77*(-t78+P[4][5]*t8+P[4][0]*t15+P[4][6]*t12+P[4][1]*t18+P[4][2]*t22-P[4][3]*t25);
Kfusion[5] = t77*(t69-P[5][4]*t3+P[5][0]*t15+P[5][6]*t12+P[5][1]*t18+P[5][2]*t22-P[5][3]*t25);
Kfusion[6] = t77*(t38-P[6][4]*t3+P[6][5]*t8+P[6][0]*t15+P[6][1]*t18+P[6][2]*t22-P[6][3]*t25);
Kfusion[7] = t77*(-P[7][4]*t3+P[7][5]*t8+P[7][0]*t15+P[7][6]*t12+P[7][1]*t18+P[7][2]*t22-P[7][3]*t25);
Kfusion[8] = t77*(-P[8][4]*t3+P[8][5]*t8+P[8][0]*t15+P[8][6]*t12+P[8][1]*t18+P[8][2]*t22-P[8][3]*t25);
Kfusion[9] = t77*(-P[9][4]*t3+P[9][5]*t8+P[9][0]*t15+P[9][6]*t12+P[9][1]*t18+P[9][2]*t22-P[9][3]*t25);
if (!inhibitDelAngBiasStates) {
Kfusion[10] = t77*(-P[10][4]*t3+P[10][5]*t8+P[10][0]*t15+P[10][6]*t12+P[10][1]*t18+P[10][2]*t22-P[10][3]*t25);
Kfusion[11] = t77*(-P[11][4]*t3+P[11][5]*t8+P[11][0]*t15+P[11][6]*t12+P[11][1]*t18+P[11][2]*t22-P[11][3]*t25);
Kfusion[12] = t77*(-P[12][4]*t3+P[12][5]*t8+P[12][0]*t15+P[12][6]*t12+P[12][1]*t18+P[12][2]*t22-P[12][3]*t25);
} else {
// zero indexes 10 to 12 = 3*4 bytes
memset(&Kfusion[10], 0, 12);
}
if (!inhibitDelVelBiasStates) {
Kfusion[13] = t77*(-P[13][4]*t3+P[13][5]*t8+P[13][0]*t15+P[13][6]*t12+P[13][1]*t18+P[13][2]*t22-P[13][3]*t25);
Kfusion[14] = t77*(-P[14][4]*t3+P[14][5]*t8+P[14][0]*t15+P[14][6]*t12+P[14][1]*t18+P[14][2]*t22-P[14][3]*t25);
Kfusion[15] = t77*(-P[15][4]*t3+P[15][5]*t8+P[15][0]*t15+P[15][6]*t12+P[15][1]*t18+P[15][2]*t22-P[15][3]*t25);
} else {
// zero indexes 13 to 15 = 3*4 bytes
memset(&Kfusion[13], 0, 12);
}
if (!inhibitMagStates) {
Kfusion[16] = t77*(-P[16][4]*t3+P[16][5]*t8+P[16][0]*t15+P[16][6]*t12+P[16][1]*t18+P[16][2]*t22-P[16][3]*t25);
Kfusion[17] = t77*(-P[17][4]*t3+P[17][5]*t8+P[17][0]*t15+P[17][6]*t12+P[17][1]*t18+P[17][2]*t22-P[17][3]*t25);
Kfusion[18] = t77*(-P[18][4]*t3+P[18][5]*t8+P[18][0]*t15+P[18][6]*t12+P[18][1]*t18+P[18][2]*t22-P[18][3]*t25);
Kfusion[19] = t77*(-P[19][4]*t3+P[19][5]*t8+P[19][0]*t15+P[19][6]*t12+P[19][1]*t18+P[19][2]*t22-P[19][3]*t25);
Kfusion[20] = t77*(-P[20][4]*t3+P[20][5]*t8+P[20][0]*t15+P[20][6]*t12+P[20][1]*t18+P[20][2]*t22-P[20][3]*t25);
Kfusion[21] = t77*(-P[21][4]*t3+P[21][5]*t8+P[21][0]*t15+P[21][6]*t12+P[21][1]*t18+P[21][2]*t22-P[21][3]*t25);
} else {
// zero indexes 16 to 21 = 6*4 bytes
memset(&Kfusion[16], 0, 24);
}
if (!inhibitWindStates) {
Kfusion[22] = t77*(-P[22][4]*t3+P[22][5]*t8+P[22][0]*t15+P[22][6]*t12+P[22][1]*t18+P[22][2]*t22-P[22][3]*t25);
Kfusion[23] = t77*(-P[23][4]*t3+P[23][5]*t8+P[23][0]*t15+P[23][6]*t12+P[23][1]*t18+P[23][2]*t22-P[23][3]*t25);
} else {
// zero indexes 22 to 23 = 2*4 bytes
memset(&Kfusion[22], 0, 8);
}
} else if (obsIndex == 2) {
// calculate Z axis observation Jacobian
H_VEL[0] = q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f;
H_VEL[1] = q1*vd*-2.0f-q0*ve*2.0f+q3*vn*2.0f;
H_VEL[2] = q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f;
H_VEL[3] = q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f;
H_VEL[4] = q0*q2*2.0f+q1*q3*2.0f;
H_VEL[5] = q0*q1*-2.0f+q2*q3*2.0f;
H_VEL[6] = q0*q0-q1*q1-q2*q2+q3*q3;
for (uint8_t index = 7; index < 24; index++) {
H_VEL[index] = 0.0f;
}
// calculate intermediate expressions for Z axis Kalman gains
float R_VEL = sq(bodyOdmDataDelayed.velErr);
float t2 = q0*q2*2.0f;
float t3 = q1*q3*2.0f;
float t4 = t2+t3;
float t5 = q0*q0;
float t6 = q1*q1;
float t7 = q2*q2;
float t8 = q3*q3;
float t9 = t5-t6-t7+t8;
float t10 = q0*q1*2.0f;
float t25 = q2*q3*2.0f;
float t11 = t10-t25;
float t12 = q0*vd*2.0f;
float t13 = q2*vn*2.0f;
float t26 = q1*ve*2.0f;
float t14 = t12+t13-t26;
float t15 = q1*vd*2.0f;
float t16 = q0*ve*2.0f;
float t27 = q3*vn*2.0f;
float t17 = t15+t16-t27;
float t18 = q3*ve*2.0f;
float t19 = q0*vn*2.0f;
float t28 = q2*vd*2.0f;
float t20 = t18+t19-t28;
float t21 = q3*vd*2.0f;
float t22 = q2*ve*2.0f;
float t23 = q1*vn*2.0f;
float t24 = t21+t22+t23;
float t29 = P[0][0]*t14;
float t30 = P[6][4]*t9;
float t31 = P[4][4]*t4;
float t32 = P[0][4]*t14;
float t33 = P[2][4]*t20;
float t34 = P[3][4]*t24;
float t78 = P[5][4]*t11;
float t79 = P[1][4]*t17;
float t35 = t30+t31+t32+t33+t34-t78-t79;
float t36 = t4*t35;
float t37 = P[6][5]*t9;
float t38 = P[4][5]*t4;
float t39 = P[0][5]*t14;
float t40 = P[2][5]*t20;
float t41 = P[3][5]*t24;
float t80 = P[5][5]*t11;
float t81 = P[1][5]*t17;
float t42 = t37+t38+t39+t40+t41-t80-t81;
float t43 = P[6][0]*t9;
float t44 = P[4][0]*t4;
float t45 = P[2][0]*t20;
float t46 = P[3][0]*t24;
float t83 = P[5][0]*t11;
float t84 = P[1][0]*t17;
float t47 = t29+t43+t44+t45+t46-t83-t84;
float t48 = t14*t47;
float t49 = P[6][1]*t9;
float t50 = P[4][1]*t4;
float t51 = P[0][1]*t14;
float t52 = P[2][1]*t20;
float t53 = P[3][1]*t24;
float t85 = P[5][1]*t11;
float t86 = P[1][1]*t17;
float t54 = t49+t50+t51+t52+t53-t85-t86;
float t55 = P[6][2]*t9;
float t56 = P[4][2]*t4;
float t57 = P[0][2]*t14;
float t58 = P[2][2]*t20;
float t59 = P[3][2]*t24;
float t88 = P[5][2]*t11;
float t89 = P[1][2]*t17;
float t60 = t55+t56+t57+t58+t59-t88-t89;
float t61 = t20*t60;
float t62 = P[6][3]*t9;
float t63 = P[4][3]*t4;
float t64 = P[0][3]*t14;
float t65 = P[2][3]*t20;
float t66 = P[3][3]*t24;
float t90 = P[5][3]*t11;
float t91 = P[1][3]*t17;
float t67 = t62+t63+t64+t65+t66-t90-t91;
float t68 = t24*t67;
float t69 = P[6][6]*t9;
float t70 = P[4][6]*t4;
float t71 = P[0][6]*t14;
float t72 = P[2][6]*t20;
float t73 = P[3][6]*t24;
float t92 = P[5][6]*t11;
float t93 = P[1][6]*t17;
float t74 = t69+t70+t71+t72+t73-t92-t93;
float t75 = t9*t74;
float t82 = t11*t42;
float t87 = t17*t54;
float t76 = R_VEL+t36+t48+t61+t68+t75-t82-t87;
float t77;
// calculate innovation variance for Z axis observation and protect against a badly conditioned calculation
if (t76 > R_VEL) {
t77 = 1.0f/t76;
faultStatus.bad_zvel = false;
} else {
t76 = R_VEL;
t77 = 1.0f/R_VEL;
faultStatus.bad_zvel = true;
return;
}
varInnovBodyVel[2] = t77;
// calculate innovation for Z axis observation
innovBodyVel[2] = bodyVelPred.z - bodyOdmDataDelayed.vel.z;
// calculate Kalman gains for X-axis observation
Kfusion[0] = t77*(t29+P[0][4]*t4+P[0][6]*t9-P[0][5]*t11-P[0][1]*t17+P[0][2]*t20+P[0][3]*t24);
Kfusion[1] = t77*(P[1][4]*t4+P[1][0]*t14+P[1][6]*t9-P[1][5]*t11-P[1][1]*t17+P[1][2]*t20+P[1][3]*t24);
Kfusion[2] = t77*(t58+P[2][4]*t4+P[2][0]*t14+P[2][6]*t9-P[2][5]*t11-P[2][1]*t17+P[2][3]*t24);
Kfusion[3] = t77*(t66+P[3][4]*t4+P[3][0]*t14+P[3][6]*t9-P[3][5]*t11-P[3][1]*t17+P[3][2]*t20);
Kfusion[4] = t77*(t31+P[4][0]*t14+P[4][6]*t9-P[4][5]*t11-P[4][1]*t17+P[4][2]*t20+P[4][3]*t24);
Kfusion[5] = t77*(-t80+P[5][4]*t4+P[5][0]*t14+P[5][6]*t9-P[5][1]*t17+P[5][2]*t20+P[5][3]*t24);
Kfusion[6] = t77*(t69+P[6][4]*t4+P[6][0]*t14-P[6][5]*t11-P[6][1]*t17+P[6][2]*t20+P[6][3]*t24);
Kfusion[7] = t77*(P[7][4]*t4+P[7][0]*t14+P[7][6]*t9-P[7][5]*t11-P[7][1]*t17+P[7][2]*t20+P[7][3]*t24);
Kfusion[8] = t77*(P[8][4]*t4+P[8][0]*t14+P[8][6]*t9-P[8][5]*t11-P[8][1]*t17+P[8][2]*t20+P[8][3]*t24);
Kfusion[9] = t77*(P[9][4]*t4+P[9][0]*t14+P[9][6]*t9-P[9][5]*t11-P[9][1]*t17+P[9][2]*t20+P[9][3]*t24);
if (!inhibitDelAngBiasStates) {
Kfusion[10] = t77*(P[10][4]*t4+P[10][0]*t14+P[10][6]*t9-P[10][5]*t11-P[10][1]*t17+P[10][2]*t20+P[10][3]*t24);
Kfusion[11] = t77*(P[11][4]*t4+P[11][0]*t14+P[11][6]*t9-P[11][5]*t11-P[11][1]*t17+P[11][2]*t20+P[11][3]*t24);
Kfusion[12] = t77*(P[12][4]*t4+P[12][0]*t14+P[12][6]*t9-P[12][5]*t11-P[12][1]*t17+P[12][2]*t20+P[12][3]*t24);
} else {
// zero indexes 10 to 12 = 3*4 bytes
memset(&Kfusion[10], 0, 12);
}
if (!inhibitDelVelBiasStates) {
Kfusion[13] = t77*(P[13][4]*t4+P[13][0]*t14+P[13][6]*t9-P[13][5]*t11-P[13][1]*t17+P[13][2]*t20+P[13][3]*t24);
Kfusion[14] = t77*(P[14][4]*t4+P[14][0]*t14+P[14][6]*t9-P[14][5]*t11-P[14][1]*t17+P[14][2]*t20+P[14][3]*t24);
Kfusion[15] = t77*(P[15][4]*t4+P[15][0]*t14+P[15][6]*t9-P[15][5]*t11-P[15][1]*t17+P[15][2]*t20+P[15][3]*t24);
} else {
// zero indexes 13 to 15 = 3*4 bytes
memset(&Kfusion[13], 0, 12);
}
if (!inhibitMagStates) {
Kfusion[16] = t77*(P[16][4]*t4+P[16][0]*t14+P[16][6]*t9-P[16][5]*t11-P[16][1]*t17+P[16][2]*t20+P[16][3]*t24);
Kfusion[17] = t77*(P[17][4]*t4+P[17][0]*t14+P[17][6]*t9-P[17][5]*t11-P[17][1]*t17+P[17][2]*t20+P[17][3]*t24);
Kfusion[18] = t77*(P[18][4]*t4+P[18][0]*t14+P[18][6]*t9-P[18][5]*t11-P[18][1]*t17+P[18][2]*t20+P[18][3]*t24);
Kfusion[19] = t77*(P[19][4]*t4+P[19][0]*t14+P[19][6]*t9-P[19][5]*t11-P[19][1]*t17+P[19][2]*t20+P[19][3]*t24);
Kfusion[20] = t77*(P[20][4]*t4+P[20][0]*t14+P[20][6]*t9-P[20][5]*t11-P[20][1]*t17+P[20][2]*t20+P[20][3]*t24);
Kfusion[21] = t77*(P[21][4]*t4+P[21][0]*t14+P[21][6]*t9-P[21][5]*t11-P[21][1]*t17+P[21][2]*t20+P[21][3]*t24);
} else {
// zero indexes 16 to 21 = 6*4 bytes
memset(&Kfusion[16], 0, 24);
}
if (!inhibitWindStates) {
Kfusion[22] = t77*(P[22][4]*t4+P[22][0]*t14+P[22][6]*t9-P[22][5]*t11-P[22][1]*t17+P[22][2]*t20+P[22][3]*t24);
Kfusion[23] = t77*(P[23][4]*t4+P[23][0]*t14+P[23][6]*t9-P[23][5]*t11-P[23][1]*t17+P[23][2]*t20+P[23][3]*t24);
} else {
// zero indexes 22 to 23 = 2*4 bytes
memset(&Kfusion[22], 0, 8);
}
} else {
return;
}
// calculate the innovation consistency test ratio
// TODO add tuning parameter for gate
bodyVelTestRatio[obsIndex] = sq(innovBodyVel[obsIndex]) / (sq(5.0f) * varInnovBodyVel[obsIndex]);
// Check the innovation for consistency and don't fuse if out of bounds
// TODO also apply angular velocity magnitude check
if ((bodyVelTestRatio[obsIndex]) < 1.0f) {
// record the last time observations were accepted for fusion
prevBodyVelFuseTime_ms = imuSampleTime_ms;
// notify first time only
if (!bodyVelFusionActive) {
bodyVelFusionActive = true;
gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u fusing odometry",(unsigned)imu_index);
}
// 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<=6; j++) {
KH[i][j] = Kfusion[i] * H_VEL[j];
}
for (unsigned j = 7; j<=stateIndexLim; j++) {
KH[i][j] = 0.0f;
}
}
for (unsigned j = 0; j<=stateIndexLim; j++) {
for (unsigned i = 0; i<=stateIndexLim; i++) {
ftype res = 0;
res += KH[i][0] * P[0][j];
res += KH[i][1] * P[1][j];
res += KH[i][2] * P[2][j];
res += KH[i][3] * P[3][j];
res += KH[i][4] * P[4][j];
res += KH[i][5] * P[5][j];
res += KH[i][6] * P[6][j];
KHP[i][j] = res;
}
}
// Check that we are not going to drive any variances negative and skip the update if so
bool healthyFusion = true;
for (uint8_t i= 0; i<=stateIndexLim; i++) {
if (KHP[i][i] > P[i][i]) {
healthyFusion = false;
}
}
if (healthyFusion) {
// update the covariance matrix
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
}
}
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-conditioning.
ForceSymmetry();
ConstrainVariances();
// correct the state vector
for (uint8_t j= 0; j<=stateIndexLim; j++) {
statesArray[j] = statesArray[j] - Kfusion[j] * innovBodyVel[obsIndex];
}
stateStruct.quat.normalize();
} else {
// record bad axis
if (obsIndex == 0) {
faultStatus.bad_xvel = true;
} else if (obsIndex == 1) {
faultStatus.bad_yvel = true;
} else if (obsIndex == 2) {
faultStatus.bad_zvel = true;
}
}
}
}
}
// select fusion of body odometry measurements
void NavEKF3_core::SelectBodyOdomFusion()
{
// Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz
// If so, don't fuse measurements on this time step to reduce frame over-runs
// Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements
if (magFusePerformed && (dtIMUavg < 0.005f) && !bodyVelFusionDelayed) {
bodyVelFusionDelayed = true;
return;
} else {
bodyVelFusionDelayed = false;
}
// Check for data at the fusion time horizon
if (storedBodyOdm.recall(bodyOdmDataDelayed, imuDataDelayed.time_ms)) {
// start performance timer
hal.util->perf_begin(_perf_FuseBodyOdom);
usingWheelSensors = false;
// Fuse data into the main filter
FuseBodyVel();
// stop the performance timer
hal.util->perf_end(_perf_FuseBodyOdom);
} else if (storedWheelOdm.recall(wheelOdmDataDelayed, imuDataDelayed.time_ms)) {
// check if the delta time is too small to calculate a velocity
if (wheelOdmDataDelayed.delTime > EKF_TARGET_DT) {
// get the forward velocity
float fwdSpd = wheelOdmDataDelayed.delAng * wheelOdmDataDelayed.radius * (1.0f / wheelOdmDataDelayed.delTime);
// get the unit vector from the projection of the X axis onto the horizontal
Vector3f unitVec;
unitVec.x = prevTnb.a.x;
unitVec.y = prevTnb.a.y;
unitVec.z = 0.0f;
unitVec.normalize();
// multiply by forward speed to get velocity vector measured by wheel encoders
Vector3f velNED = unitVec * fwdSpd;
// This is a hack to enable use of the existing body frame velocity fusion method
// TODO write a dedicated observation model for wheel encoders
usingWheelSensors = true;
bodyOdmDataDelayed.vel = prevTnb * velNED;
bodyOdmDataDelayed.body_offset = wheelOdmDataDelayed.hub_offset;
bodyOdmDataDelayed.velErr = frontend->_wencOdmVelErr;
// Fuse data into the main filter
FuseBodyVel();
}
}
}