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ardupilot/libraries/AP_NavEKF3/AP_NavEKF3_PosVelFusion.cpp

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#include <AP_HAL/AP_HAL.h>
#include "AP_NavEKF3.h"
#include "AP_NavEKF3_core.h"
#include <GCS_MAVLink/GCS.h>
#include <AP_DAL/AP_DAL.h>
/********************************************************
* 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(resetDataSource velResetSource)
{
// Store the velocity 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 == resetDataSource::DEFAULT) || velResetSource == resetDataSource::GPS) {
// correct for antenna position
gps_elements gps_corrected = gpsDataNew;
CorrectGPSForAntennaOffset(gps_corrected);
stateStruct.velocity.x = gps_corrected.vel.x;
stateStruct.velocity.y = gps_corrected.vel.y;
// set the variances using the reported GPS speed accuracy
P[5][5] = P[4][4] = sq(MAX(frontend->_gpsHorizVelNoise,gpsSpdAccuracy));
#if EK3_FEATURE_EXTERNAL_NAV
} else if ((imuSampleTime_ms - extNavVelMeasTime_ms < 250 && velResetSource == resetDataSource::DEFAULT) || velResetSource == resetDataSource::EXTNAV) {
// use external nav data as the 2nd preference
// already corrected for sensor position
stateStruct.velocity.x = extNavVelDelayed.vel.x;
stateStruct.velocity.y = extNavVelDelayed.vel.y;
P[5][5] = P[4][4] = sq(extNavVelDelayed.err);
#endif // EK3_FEATURE_EXTERNAL_NAV
} 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 velocity 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;
}
// resets position states to last GPS measurement or to zero if in constant position mode
void NavEKF3_core::ResetPosition(resetDataSource posResetSource)
{
// 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 == resetDataSource::DEFAULT) || posResetSource == resetDataSource::GPS) {
// correct for antenna position
gps_elements gps_corrected = gpsDataNew;
CorrectGPSForAntennaOffset(gps_corrected);
// record the ID of the GPS for the data we are using for the reset
last_gps_idx = gps_corrected.sensor_idx;
// calculate position
const Location gpsloc{gps_corrected.lat, gps_corrected.lng, 0, Location::AltFrame::ABSOLUTE};
stateStruct.position.xy() = EKF_origin.get_distance_NE_ftype(gpsloc);
// compensate for offset between last GPS measurement and the EKF time horizon. Note that this is an unusual
// time delta in that it can be both -ve and +ve
const int32_t tdiff = imuDataDelayed.time_ms - gps_corrected.time_ms;
stateStruct.position.xy() += gps_corrected.vel.xy()*0.001*tdiff;
// set the variances using the position measurement noise parameter
P[7][7] = P[8][8] = sq(MAX(gpsPosAccuracy,frontend->_gpsHorizPosNoise));
} else if ((imuSampleTime_ms - rngBcnLast3DmeasTime_ms < 250 && posResetSource == resetDataSource::DEFAULT) || posResetSource == resetDataSource::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];
#if EK3_FEATURE_EXTERNAL_NAV
} else if ((imuSampleTime_ms - extNavDataDelayed.time_ms < 250 && posResetSource == resetDataSource::DEFAULT) || posResetSource == resetDataSource::EXTNAV) {
// use external nav data as the third preference
stateStruct.position.x = extNavDataDelayed.pos.x;
stateStruct.position.y = extNavDataDelayed.pos.y;
// set the variances as received from external nav system data
P[7][7] = P[8][8] = sq(extNavDataDelayed.posErr);
#endif // EK3_FEATURE_EXTERNAL_NAV
}
}
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 the timeout flags and counters
posTimeout = false;
lastPosPassTime_ms = imuSampleTime_ms;
}
// reset the stateStruct's NE position to the specified position
// posResetNE is updated to hold the change in position
// storedOutput, outputDataNew and outputDataDelayed are updated with the change in position
// lastPosReset_ms is updated with the time of the reset
void NavEKF3_core::ResetPositionNE(ftype posN, ftype posE)
{
// Store the position before the reset so that we can record the reset delta
const Vector3F posOrig = stateStruct.position;
// Set the position states to the new position
stateStruct.position.x = posN;
stateStruct.position.y = posE;
// Calculate the position offset due to the reset
posResetNE.x = stateStruct.position.x - posOrig.x;
posResetNE.y = stateStruct.position.y - posOrig.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;
}
// reset the stateStruct's D position
// posResetD is updated to hold the change in position
// storedOutput, outputDataNew and outputDataDelayed are updated with the change in position
// lastPosResetD_ms is updated with the time of the reset
void NavEKF3_core::ResetPositionD(ftype posD)
{
// Store the position before the reset so that we can record the reset delta
const ftype posDOrig = stateStruct.position.z;
// write to the state vector
stateStruct.position.z = posD;
// Calculate the position jump due to the reset
posResetD = stateStruct.position.z - posDOrig;
// Add the offset to the output observer states
outputDataNew.position.z += posResetD;
vertCompFiltState.pos = outputDataNew.position.z;
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;
}
// 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;
}
vertCompFiltState.pos = 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 &&
(gpsIsInUse || badIMUdata) &&
frontend->sources.useVelZSource(AP_NavEKF_Source::SourceZ::GPS) &&
gpsDataNew.have_vz &&
(imuSampleTime_ms - gpsDataDelayed.time_ms < 500)) {
stateStruct.velocity.z = gpsDataNew.vel.z;
#if EK3_FEATURE_EXTERNAL_NAV
} else if (inFlight && useExtNavVel && (activeHgtSource == AP_NavEKF_Source::SourceZ::EXTNAV)) {
stateStruct.velocity.z = extNavVelDelayed.vel.z;
#endif
} 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;
vertCompFiltState.vel = outputDataNew.velocity.z;
// reset the corresponding covariances
zeroRows(P,6,6);
zeroCols(P,6,6);
// set the variances to the measurement variance
#if EK3_FEATURE_EXTERNAL_NAV
if (useExtNavVel) {
P[6][6] = sq(extNavVelDelayed.err);
} else
#endif
{
P[6][6] = sq(frontend->_gpsVertVelNoise);
}
vertVelVarClipCounter = 0;
}
// Zero the EKF height datum
// Return true if the height datum reset has been performed
bool NavEKF3_core::resetHeightDatum(void)
{
if (activeHgtSource == AP_NavEKF_Source::SourceZ::RANGEFINDER || !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
ftype oldHgt = -stateStruct.position.z;
// reset the barometer so that it reads zero at the current height
dal.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
EKF_origin.alt += (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
EKF_origin.alt = dal.gps().location().alt;
}
ekfGpsRefHgt = (double)0.01 * (double)EKF_origin.alt;
}
// set the terrain state to zero (on ground). The adjustment for
// frame height will get added in the later constraints
terrainState = 0;
return true;
}
/*
correct GPS data for position offset of antenna phase centre relative to the IMU
*/
void NavEKF3_core::CorrectGPSForAntennaOffset(gps_elements &gps_data) const
{
// return immediately if already corrected
if (gps_data.corrected) {
return;
}
gps_data.corrected = true;
const Vector3F posOffsetBody = dal.gps().get_antenna_offset(gps_data.sensor_idx).toftype() - accelPosOffset;
if (posOffsetBody.is_zero()) {
return;
}
// 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);
gps_data.vel -= velOffsetEarth;
Vector3F posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
Location::offset_latlng(gps_data.lat, gps_data.lng, -posOffsetEarth.x, -posOffsetEarth.y);
gps_data.hgt += posOffsetEarth.z;
}
// correct external navigation earth-frame position using sensor body-frame offset
void NavEKF3_core::CorrectExtNavForSensorOffset(ext_nav_elements &ext_nav_data)
{
// return immediately if already corrected
if (ext_nav_data.corrected) {
return;
}
ext_nav_data.corrected = true;
// external nav data is against the public_origin, so convert to offset from EKF_origin
ext_nav_data.pos.xy() += EKF_origin.get_distance_NE_ftype(public_origin);
#if HAL_VISUALODOM_ENABLED
const auto *visual_odom = dal.visualodom();
if (visual_odom == nullptr) {
return;
}
const Vector3F posOffsetBody = visual_odom->get_pos_offset().toftype() - accelPosOffset;
if (posOffsetBody.is_zero()) {
return;
}
Vector3F posOffsetEarth = prevTnb.mul_transpose(posOffsetBody);
ext_nav_data.pos.x -= posOffsetEarth.x;
ext_nav_data.pos.y -= posOffsetEarth.y;
ext_nav_data.pos.z -= posOffsetEarth.z;
#endif
}
// correct external navigation earth-frame velocity using sensor body-frame offset
void NavEKF3_core::CorrectExtNavVelForSensorOffset(ext_nav_vel_elements &ext_nav_vel_data) const
{
// return immediately if already corrected
if (ext_nav_vel_data.corrected) {
return;
}
ext_nav_vel_data.corrected = true;
#if HAL_VISUALODOM_ENABLED
const auto *visual_odom = dal.visualodom();
if (visual_odom == nullptr) {
return;
}
const Vector3F posOffsetBody = visual_odom->get_pos_offset().toftype() - accelPosOffset;
if (posOffsetBody.is_zero()) {
return;
}
// TODO use a filtered angular rate with a group delay that matches the sensor delay
const Vector3F angRate = imuDataDelayed.delAng * (1.0/imuDataDelayed.delAngDT);
ext_nav_vel_data.vel += get_vel_correction_for_sensor_offset(posOffsetBody, prevTnb, angRate);
#endif
}
// calculate velocity variance helper function
void NavEKF3_core::CalculateVelInnovationsAndVariances(const Vector3F &velocity, ftype noise, ftype accel_scale, Vector3F &innovations, Vector3F &variances) const
{
// innovations are latest estimate - latest observation
innovations = stateStruct.velocity - velocity;
const ftype obs_data_chk = sq(constrain_ftype(noise, 0.05, 5.0)) + sq(accel_scale * accNavMag);
// calculate innovation variance. velocity states start at index 4
variances.x = P[4][4] + obs_data_chk;
variances.y = P[5][5] + obs_data_chk;
variances.z = P[6][6] + obs_data_chk;
}
/********************************************************
* 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;
}
#if EK3_FEATURE_EXTERNAL_NAV
// Check for data at the fusion time horizon
extNavDataToFuse = storedExtNav.recall(extNavDataDelayed, imuDataDelayed.time_ms);
if (extNavDataToFuse) {
CorrectExtNavForSensorOffset(extNavDataDelayed);
}
extNavVelToFuse = storedExtNavVel.recall(extNavVelDelayed, imuDataDelayed.time_ms);
if (extNavVelToFuse) {
CorrectExtNavVelForSensorOffset(extNavVelDelayed);
// calculate innovations and variances for reporting purposes only
CalculateVelInnovationsAndVariances(extNavVelDelayed.vel, extNavVelDelayed.err, frontend->extNavVelVarAccScale, extNavVelInnov, extNavVelVarInnov);
// record time innovations were calculated (for timeout checks)
extNavVelInnovTime_ms = dal.millis();
}
#endif // EK3_FEATURE_EXTERNAL_NAV
// Read GPS data from the sensor
readGpsData();
readGpsYawData();
// get data that has now fallen behind the fusion time horizon
gpsDataToFuse = storedGPS.recall(gpsDataDelayed,imuDataDelayed.time_ms);
if (gpsDataToFuse) {
CorrectGPSForAntennaOffset(gpsDataDelayed);
// calculate innovations and variances for reporting purposes only
CalculateVelInnovationsAndVariances(gpsDataDelayed.vel, frontend->_gpsHorizVelNoise, frontend->gpsNEVelVarAccScale, gpsVelInnov, gpsVelVarInnov);
// record time innovations were calculated (for timeout checks)
gpsVelInnovTime_ms = dal.millis();
}
// detect position source changes. Trigger position reset if position source is valid
const AP_NavEKF_Source::SourceXY posxy_source = frontend->sources.getPosXYSource();
if (posxy_source != posxy_source_last) {
posxy_source_reset = (posxy_source != AP_NavEKF_Source::SourceXY::NONE);
posxy_source_last = posxy_source;
}
// initialise all possible data we may fuse
fusePosData = false;
fuseVelData = false;
// Determine if we need to fuse position and velocity data on this time step
if (gpsDataToFuse && (PV_AidingMode == AID_ABSOLUTE) && (posxy_source == AP_NavEKF_Source::SourceXY::GPS)) {
// Don't fuse velocity data if GPS doesn't support it
fuseVelData = frontend->sources.useVelXYSource(AP_NavEKF_Source::SourceXY::GPS);
fusePosData = true;
#if EK3_FEATURE_EXTERNAL_NAV
extNavUsedForPos = false;
#endif
// copy corrected GPS data to observation vector
if (fuseVelData) {
velPosObs[0] = gpsDataDelayed.vel.x;
velPosObs[1] = gpsDataDelayed.vel.y;
velPosObs[2] = gpsDataDelayed.vel.z;
}
const Location gpsloc{gpsDataDelayed.lat, gpsDataDelayed.lng, 0, Location::AltFrame::ABSOLUTE};
const Vector2F posxy = EKF_origin.get_distance_NE_ftype(gpsloc);
velPosObs[3] = posxy.x;
velPosObs[4] = posxy.y;
#if EK3_FEATURE_EXTERNAL_NAV
} else if (extNavDataToFuse && (PV_AidingMode == AID_ABSOLUTE) && (posxy_source == AP_NavEKF_Source::SourceXY::EXTNAV)) {
// use external nav system for horizontal position
extNavUsedForPos = true;
fusePosData = true;
velPosObs[3] = extNavDataDelayed.pos.x;
velPosObs[4] = extNavDataDelayed.pos.y;
#endif // EK3_FEATURE_EXTERNAL_NAV
}
#if EK3_FEATURE_EXTERNAL_NAV
// fuse external navigation velocity data if available
// extNavVelDelayed is already corrected for sensor position
if (extNavVelToFuse && frontend->sources.useVelXYSource(AP_NavEKF_Source::SourceXY::EXTNAV)) {
fuseVelData = true;
velPosObs[0] = extNavVelDelayed.vel.x;
velPosObs[1] = extNavVelDelayed.vel.y;
velPosObs[2] = extNavVelDelayed.vel.z;
}
#endif
// 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) && (posxy_source == AP_NavEKF_Source::SourceXY::GPS) && (gpsDataDelayed.sensor_idx != last_gps_idx || posxy_source_reset)) {
// mark a source reset as consumed
posxy_source_reset = false;
// record the ID of the GPS that we are using for the reset
last_gps_idx = gpsDataDelayed.sensor_idx;
// reset the position to the GPS position
const Location gpsloc{gpsDataDelayed.lat, gpsDataDelayed.lng, 0, Location::AltFrame::ABSOLUTE};
const Vector2F posxy = EKF_origin.get_distance_NE_ftype(gpsloc);
ResetPositionNE(posxy.x, posxy.y);
// If we are also using GPS as the height reference, reset the height
if (activeHgtSource == AP_NavEKF_Source::SourceZ::GPS) {
ResetPositionD(-hgtMea);
}
}
#if EK3_FEATURE_EXTERNAL_NAV
// check for external nav position reset
if (extNavDataToFuse && (PV_AidingMode == AID_ABSOLUTE) && (posxy_source == AP_NavEKF_Source::SourceXY::EXTNAV) && (extNavDataDelayed.posReset || posxy_source_reset)) {
// mark a source reset as consumed
posxy_source_reset = false;
ResetPositionNE(extNavDataDelayed.pos.x, extNavDataDelayed.pos.y);
if (activeHgtSource == AP_NavEKF_Source::SourceZ::EXTNAV) {
ResetPositionD(-hgtMea);
}
}
#endif // EK3_FEATURE_EXTERNAL_NAV
// If we are operating without any aiding, fuse in constant position of constant
// velocity measurements to constrain tilt drift. This assumes a non-manoeuvring
// vehicle. Do this to coincide with the height fusion.
if (fuseHgtData && PV_AidingMode == AID_NONE) {
if (assume_zero_sideslip() && tiltAlignComplete && motorsArmed) {
// handle special case where we are launching a FW aircraft without magnetometer
fusePosData = false;
velPosObs[0] = 0.0f;
velPosObs[1] = 0.0f;
velPosObs[2] = stateStruct.velocity.z;
bool resetVelNE = !prevMotorsArmed;
// reset states to stop launch accel causing tilt error
if (imuDataDelayed.delVel.x > 1.1f * GRAVITY_MSS * imuDataDelayed.delVelDT) {
lastLaunchAccelTime_ms = imuSampleTime_ms;
fuseVelData = false;
resetVelNE = true;
} else if (lastLaunchAccelTime_ms != 0 && (imuSampleTime_ms - lastLaunchAccelTime_ms) < 10000) {
fuseVelData = false;
resetVelNE = true;
} else {
fuseVelData = true;
}
if (resetVelNE) {
stateStruct.velocity.x = 0.0f;
stateStruct.velocity.y = 0.0f;
}
} else {
fusePosData = true;
fuseVelData = false;
velPosObs[3] = lastKnownPositionNE.x;
velPosObs[4] = lastKnownPositionNE.y;
}
}
// 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()
{
// health is set bad until test passed
bool velCheckPassed = false; // boolean true if velocity measurements have passed innovation consistency checks
bool posCheckPassed = false; // boolean true if position measurements have passed innovation consistency check
bool hgtCheckPassed = false; // boolean true if height measurements have passed innovation consistency check
// declare variables used to control access to arrays
bool fuseData[6] {};
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
ftype 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) {
// calculate additional error in GPS position caused by manoeuvring
ftype posErr = frontend->gpsPosVarAccScale * accNavMag;
// To-Do: this posErr should come from external nav when fusing external nav position
// 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_ftype(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_ftype(gpsSpdAccuracy, frontend->_gpsHorizVelNoise, 50.0f));
R_OBS[2] = sq(constrain_ftype(gpsSpdAccuracy, frontend->_gpsVertVelNoise, 50.0f));
#if EK3_FEATURE_EXTERNAL_NAV
} else if (extNavVelToFuse) {
R_OBS[2] = R_OBS[0] = sq(constrain_ftype(extNavVelDelayed.err, 0.05f, 5.0f));
#endif
} else {
// calculate additional error in GPS velocity caused by manoeuvring
R_OBS[0] = sq(constrain_ftype(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
R_OBS[2] = sq(constrain_ftype(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_ftype(gpsPosAccuracy, frontend->_gpsHorizPosNoise, 100.0f));
#if EK3_FEATURE_EXTERNAL_NAV
} else if (extNavUsedForPos) {
R_OBS[3] = sq(constrain_ftype(extNavDataDelayed.posErr, 0.01f, 10.0f));
#endif
} else {
R_OBS[3] = sq(constrain_ftype(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
ftype obs_data_chk;
#if EK3_FEATURE_EXTERNAL_NAV
if (extNavVelToFuse) {
obs_data_chk = sq(constrain_ftype(extNavVelDelayed.err, 0.05f, 5.0f)) + sq(frontend->extNavVelVarAccScale * accNavMag);
} else
#endif
{
obs_data_chk = sq(constrain_ftype(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
}
R_OBS_DATA_CHECKS[0] = R_OBS_DATA_CHECKS[1] = R_OBS_DATA_CHECKS[2] = obs_data_chk;
}
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 (gpsDataDelayed.have_vz && fuseVelData && (frontend->sources.getPosZSource() != AP_NavEKF_Source::SourceZ::GPS)) {
// calculate innovations for height and vertical GPS vel measurements
const ftype hgtErr = stateStruct.position.z - velPosObs[5];
const ftype velDErr = stateStruct.velocity.z - velPosObs[2];
// Check if they are the same sign and both more than 3-sigma out of bounds
// Step the test threshold up in stages from 1 to 2 to 3 sigma after exiting
// from a previous bad IMU event so that a subsequent error is caught more quickly.
const uint32_t timeSinceLastBadIMU_ms = imuSampleTime_ms - badIMUdata_ms;
float R_gain;
if (timeSinceLastBadIMU_ms > (BAD_IMU_DATA_HOLD_MS * 2)) {
R_gain = 9.0F;
} else if (timeSinceLastBadIMU_ms > ((BAD_IMU_DATA_HOLD_MS * 3) / 2)) {
R_gain = 4.0F;
} else {
R_gain = 1.0F;
}
if ((hgtErr*velDErr > 0.0f) && (sq(hgtErr) > R_gain * R_OBS[5]) && (sq(velDErr) >R_gain * R_OBS[2])) {
badIMUdata_ms = imuSampleTime_ms;
} else {
goodIMUdata_ms = imuSampleTime_ms;
}
if (timeSinceLastBadIMU_ms < BAD_IMU_DATA_HOLD_MS) {
badIMUdata = true;
stateStruct.velocity.z = gpsDataDelayed.vel.z;
} else {
badIMUdata = false;
}
}
// Test horizontal position measurements
if (fusePosData) {
innovVelPos[3] = stateStruct.position.x - velPosObs[3];
innovVelPos[4] = stateStruct.position.y - velPosObs[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
// Don't allow test to fail if not navigating and using a constant position
// assumption to constrain tilt errors because innovations can become large
// due to vehicle motion.
ftype maxPosInnov2 = sq(MAX(0.01 * (ftype)frontend->_gpsPosInnovGate, 1.0))*(varInnovVelPos[3] + varInnovVelPos[4]);
posTestRatio = (sq(innovVelPos[3]) + sq(innovVelPos[4])) / maxPosInnov2;
if (posTestRatio < 1.0f || (PV_AidingMode == AID_NONE)) {
posCheckPassed = true;
lastPosPassTime_ms = imuSampleTime_ms;
}
// Use position data if healthy or timed out or bad IMU data
// Always fuse data if bad IMU to prevent aliasing and clipping pulling the state estimate away
// from the measurement un-opposed if test threshold is exceeded.
if (posCheckPassed || posTimeout || badIMUdata) {
// if timed out or outside the specified uncertainty radius, reset to the external sensor
if (posTimeout || ((P[8][8] + P[7][7]) > sq(ftype(frontend->_gpsGlitchRadiusMax)))) {
// reset the position to the current external sensor position
ResetPosition(resetDataSource::DEFAULT);
// Don't fuse the same data we have used to reset states.
fusePosData = 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(ftype(0.5f*frontend->_gpsGlitchRadiusMax));
P[8][8] = P[7][7];
// Reset the normalised innovation to avoid failing the bad fusion tests
posTestRatio = 0.0f;
// Reset velocity if it has timed out
if (velTimeout) {
ResetVelocity(resetDataSource::DEFAULT);
// Don't fuse the same data we have used to reset states.
fuseVelData = false;
// Reset the normalised innovation to avoid failing the bad fusion tests
velTestRatio = 0.0f;
}
}
} else {
fusePosData = false;
}
}
// Test velocity measurements
if (fuseVelData) {
uint8_t imax = 2;
// Don't fuse vertical velocity observations if disabled in sources or not available
if ((!frontend->sources.haveVelZSource() || PV_AidingMode != AID_ABSOLUTE ||
!gpsDataDelayed.have_vz) && !useExtNavVel) {
imax = 1;
}
// Apply an innovation consistency threshold test
ftype innovVelSumSq = 0; // sum of squares of velocity innovations
ftype varVelSum = 0; // sum of velocity innovation variances
for (uint8_t i = 0; i<=imax; i++) {
stateIndex = i + 4;
const float innovation = stateStruct.velocity[i] - velPosObs[i];
innovVelSumSq += sq(innovation);
varInnovVelPos[i] = P[stateIndex][stateIndex] + R_OBS_DATA_CHECKS[i];
varVelSum += varInnovVelPos[i];
}
velTestRatio = innovVelSumSq / (varVelSum * sq(MAX(0.01 * (ftype)frontend->_gpsVelInnovGate, 1.0)));
if (velTestRatio < 1.0) {
velCheckPassed = true;
lastVelPassTime_ms = imuSampleTime_ms;
}
// Use velocity data if healthy, timed out or when IMU fault has been detected
// Always fuse data if bad IMU to prevent aliasing and clipping pulling the state estimate away
// from the measurement un-opposed if test threshold is exceeded.
if (velCheckPassed || velTimeout || badIMUdata) {
// If we are doing full aiding and velocity fusion times out, reset to the external sensor velocity
if (PV_AidingMode == AID_ABSOLUTE && velTimeout) {
ResetVelocity(resetDataSource::DEFAULT);
// Don't fuse the same data we have used to reset states.
fuseVelData = false;
// Reset the normalised innovation to avoid failing the bad fusion tests
velTestRatio = 0.0f;
}
} else {
fuseVelData = false;
}
}
// Test height measurements
if (fuseHgtData) {
// Calculate height innovations
innovVelPos[5] = stateStruct.position.z - velPosObs[5];
varInnovVelPos[5] = P[9][9] + R_OBS_DATA_CHECKS[5];
// Calculate the innovation consistency test ratio
hgtTestRatio = sq(innovVelPos[5]) / (sq(MAX(0.01 * (ftype)frontend->_hgtInnovGate, 1.0)) * varInnovVelPos[5]);
// When on ground we accept a larger test ratio to allow the filter to handle large switch on IMU
// bias errors without rejecting the height sensor.
const float maxTestRatio = (PV_AidingMode == AID_NONE && onGround)? 3.0f : 1.0f;
if (hgtTestRatio < maxTestRatio) {
hgtCheckPassed = true;
lastHgtPassTime_ms = imuSampleTime_ms;
}
// Use height data if innovation check passed or timed out or if bad IMU data
// Always fuse data if bad IMU to prevent aliasing and clipping pulling the state estimate away
// from the measurement un-opposed if test threshold is exceeded.
if (hgtCheckPassed || hgtTimeout || badIMUdata) {
// 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) {
ftype dtBaro = (imuSampleTime_ms - lastHgtPassTime_ms) * 1.0e-3;
const ftype hgtInnovFiltTC = 2.0;
ftype alpha = constrain_ftype(dtBaro/(dtBaro+hgtInnovFiltTC), 0.0, 1.0);
hgtInnovFiltState += (innovVelPos[5] - hgtInnovFiltState)*alpha;
} else {
hgtInnovFiltState = 0.0f;
}
if (hgtTimeout) {
ResetHeight();
// Don't fuse the same data we have used to reset states.
fuseHgtData = false;
}
} else {
fuseHgtData = false;
}
}
// set range for sequential fusion of velocity and position measurements depending on which data is available and its health
if (fuseVelData) {
fuseData[0] = true;
fuseData[1] = true;
if (useGpsVertVel || useExtNavVel) {
fuseData[2] = true;
}
}
if (fusePosData) {
fuseData[3] = true;
fuseData[4] = true;
}
if (fuseHgtData) {
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] - velPosObs[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
} else if (obsIndex == 3 || obsIndex == 4) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - velPosObs[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
} else if (obsIndex == 5) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - velPosObs[obsIndex];
const ftype gndMaxBaroErr = MAX(frontend->_baroGndEffectDeadZone, 0.0);
const ftype gndBaroInnovFloor = -0.5;
if ((dal.get_touchdown_expected() || dal.get_takeoff_expected()) && activeHgtSource == AP_NavEKF_Source::SourceZ::BARO) {
// when baro positive pressure error due to ground effect is expected,
// floor the barometer innovation at gndBaroInnovFloor
// constrain the correction between 0 and gndBaroInnovFloor+gndMaxBaroErr
// this function looks like this:
// |/
//---------|---------
// ____/|
// / |
// / |
innovVelPos[5] += constrain_ftype(-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 estimation by setting Kalman gains to zero
if (!inhibitDelAngBiasStates) {
for (uint8_t i = 10; i<=12; i++) {
// Don't try to learn gyro bias if not aiding and the axis is
// less than 45 degrees from vertical because the bias is poorly observable
bool poorObservability = false;
if (PV_AidingMode == AID_NONE) {
const uint8_t axisIndex = i - 10;
if (axisIndex == 0) {
poorObservability = fabsF(prevTnb.a.z) > M_SQRT1_2;
} else if (axisIndex == 1) {
poorObservability = fabsF(prevTnb.b.z) > M_SQRT1_2;
} else {
poorObservability = fabsF(prevTnb.c.z) > M_SQRT1_2;
}
}
if (poorObservability) {
Kfusion[i] = 0.0;
} else {
Kfusion[i] = P[i][stateIndex]*SK;
}
}
} else {
// zero indexes 10 to 12
zero_range(&Kfusion[0], 10, 12);
}
// Inhibit delta velocity bias state estimation by setting Kalman gains to zero
// Don't use 'fake' horizontal measurements used to constrain attitude drift during
// periods of non-aiding to learn bias as these can give incorrect esitmates.
const bool horizInhibit = PV_AidingMode == AID_NONE && obsIndex != 2 && obsIndex != 5;
if (!horizInhibit && !inhibitDelVelBiasStates && !badIMUdata) {
for (uint8_t i = 13; i<=15; i++) {
if (!dvelBiasAxisInhibit[i-13]) {
Kfusion[i] = P[i][stateIndex]*SK;
} else {
Kfusion[i] = 0.0f;
}
}
} else {
// zero indexes 13 to 15
zero_range(&Kfusion[0], 13, 15);
}
// 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
zero_range(&Kfusion[0], 16, 21);
}
// 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
zero_range(&Kfusion[0], 22, 23);
}
// 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;
}
}
}
}
}
}
/********************************************************
* 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
const auto *_rng = dal.rangefinder();
if (_rng && rangeDataToFuse) {
auto *sensor = _rng->get_backend(rangeDataDelayed.sensor_idx);
if (sensor != nullptr) {
Vector3F posOffsetBody = sensor->get_pos_offset().toftype() - 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);
bool rangeFinderDataIsFresh = (imuSampleTime_ms - rngValidMeaTime_ms < 500);
#if EK3_FEATURE_EXTERNAL_NAV
const bool extNavDataIsFresh = (imuSampleTime_ms - extNavMeasTime_ms < 500);
#endif
// select height source
if ((frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::RANGEFINDER) && _rng && rangeFinderDataIsFresh) {
// user has specified the range finder as a primary height source
activeHgtSource = AP_NavEKF_Source::SourceZ::RANGEFINDER;
} else if ((frontend->_useRngSwHgt > 0) && ((frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::BARO) || (frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::GPS)) && _rng && rangeFinderDataIsFresh) {
// determine if we are above or below the height switch region
ftype rangeMaxUse = 1e-4 * (ftype)_rng->max_distance_cm_orient(ROTATION_PITCH_270) * (ftype)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
ftype horizSpeed = stateStruct.velocity.xy().length();
dontTrustTerrain = (horizSpeed > frontend->_useRngSwSpd) || !terrainHgtStable;
ftype 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 == AP_NavEKF_Source::SourceZ::RANGEFINDER)) {
// cannot trust terrain or range finder so stop using range finder height
if (frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::BARO) {
activeHgtSource = AP_NavEKF_Source::SourceZ::BARO;
} else if (frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::GPS) {
activeHgtSource = AP_NavEKF_Source::SourceZ::GPS;
}
} else if (belowLowerSwHgt && trustTerrain && (prevTnb.c.z >= 0.7f)) {
// reliable terrain and range finder so start using range finder height
activeHgtSource = AP_NavEKF_Source::SourceZ::RANGEFINDER;
}
} else if (frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::BARO) {
activeHgtSource = AP_NavEKF_Source::SourceZ::BARO;
} else if ((frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::GPS) && ((imuSampleTime_ms - lastTimeGpsReceived_ms) < 500) && validOrigin && gpsAccuracyGood) {
activeHgtSource = AP_NavEKF_Source::SourceZ::GPS;
} else if ((frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::BEACON) && validOrigin && rngBcnGoodToAlign) {
activeHgtSource = AP_NavEKF_Source::SourceZ::BEACON;
#if EK3_FEATURE_EXTERNAL_NAV
} else if ((frontend->sources.getPosZSource() == AP_NavEKF_Source::SourceZ::EXTNAV) && extNavDataIsFresh) {
activeHgtSource = AP_NavEKF_Source::SourceZ::EXTNAV;
#endif
}
// Use Baro alt as a fallback if we lose range finder, GPS, external nav or Beacon
bool lostRngHgt = ((activeHgtSource == AP_NavEKF_Source::SourceZ::RANGEFINDER) && !rangeFinderDataIsFresh);
bool lostGpsHgt = ((activeHgtSource == AP_NavEKF_Source::SourceZ::GPS) && ((imuSampleTime_ms - lastTimeGpsReceived_ms) > 2000));
bool lostRngBcnHgt = ((activeHgtSource == AP_NavEKF_Source::SourceZ::BEACON) && ((imuSampleTime_ms - rngBcnDataDelayed.time_ms) > 2000));
bool fallback_to_baro = lostRngHgt || lostGpsHgt || lostRngBcnHgt;
#if EK3_FEATURE_EXTERNAL_NAV
bool lostExtNavHgt = ((activeHgtSource == AP_NavEKF_Source::SourceZ::EXTNAV) && !extNavDataIsFresh);
fallback_to_baro |= lostExtNavHgt;
#endif
if (fallback_to_baro) {
activeHgtSource = AP_NavEKF_Source::SourceZ::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 != AP_NavEKF_Source::SourceZ::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 (!dal.get_takeoff_expected()) {
const ftype gndHgtFiltTC = 0.5;
const ftype dtBaro = frontend->hgtAvg_ms*1.0e-3;
ftype alpha = constrain_ftype(dtBaro / (dtBaro+gndHgtFiltTC),0.0,1.0);
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 == AP_NavEKF_Source::SourceZ::BARO)) ||
((frontend->_originHgtMode & (1 << 1)) && (activeHgtSource == AP_NavEKF_Source::SourceZ::RANGEFINDER)))
) {
correctEkfOriginHeight();
}
// Select the height measurement source
#if EK3_FEATURE_EXTERNAL_NAV
if (extNavDataToFuse && (activeHgtSource == AP_NavEKF_Source::SourceZ::EXTNAV)) {
hgtMea = -extNavDataDelayed.pos.z;
velPosObs[5] = -hgtMea;
posDownObsNoise = sq(constrain_ftype(extNavDataDelayed.posErr, 0.1f, 10.0f));
fuseHgtData = true;
} else
#endif // EK3_FEATURE_EXTERNAL_NAV
if (rangeDataToFuse && (activeHgtSource == AP_NavEKF_Source::SourceZ::RANGEFINDER)) {
// 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;
velPosObs[5] = -hgtMea;
// enable fusion
fuseHgtData = true;
// set the observation noise
posDownObsNoise = sq(constrain_ftype(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 == AP_NavEKF_Source::SourceZ::GPS)) {
// using GPS data
hgtMea = gpsDataDelayed.hgt;
velPosObs[5] = -hgtMea;
// 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_ftype(gpsHgtAccuracy, 1.5f * frontend->_gpsHorizPosNoise, 100.0f));
} else {
posDownObsNoise = sq(constrain_ftype(1.5f * frontend->_gpsHorizPosNoise, 0.1f, 10.0f));
}
} else if (baroDataToFuse && (activeHgtSource == AP_NavEKF_Source::SourceZ::BARO)) {
// using Baro data
hgtMea = baroDataDelayed.hgt - baroHgtOffset;
// enable fusion
fuseHgtData = true;
// set the observation noise
posDownObsNoise = sq(constrain_ftype(frontend->_baroAltNoise, 0.1f, 10.0f));
// reduce weighting (increase observation noise) on baro if we are likely to be experiencing rotor wash ground interaction
if (dal.get_takeoff_expected() || dal.get_touchdown_expected()) {
posDownObsNoise *= frontend->gndEffectBaroScaler;
}
velPosObs[5] = -hgtMea;
} else {
fuseHgtData = false;
}
// detect changes in source and reset height
if ((activeHgtSource != prevHgtSource) && fuseHgtData) {
prevHgtSource = activeHgtSource;
ResetPositionD(-hgtMea);
}
// If we haven't fused height data for a while or have bad IMU data, then declare the height data as being timed out
// set height timeout period based on whether we have vertical GPS velocity available to constrain drift
hgtRetryTime_ms = ((useGpsVertVel || useExtNavVel) && !velTimeout) ? frontend->hgtRetryTimeMode0_ms : frontend->hgtRetryTimeMode12_ms;
if (imuSampleTime_ms - lastHgtPassTime_ms > hgtRetryTime_ms ||
(badIMUdata &&
(imuSampleTime_ms - goodIMUdata_ms > BAD_IMU_DATA_TIMEOUT_MS))) {
hgtTimeout = true;
} else {
hgtTimeout = false;
}
}
#if EK3_FEATURE_BODY_ODOM
/*
* 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
ftype q0 = stateStruct.quat[0];
ftype q1 = stateStruct.quat[1];
ftype q2 = stateStruct.quat[2];
ftype q3 = stateStruct.quat[3];
ftype vn = stateStruct.velocity.x;
ftype ve = stateStruct.velocity.y;
ftype 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
ftype R_VEL = sq(bodyOdmDataDelayed.velErr);
ftype t2 = q0*q3*2.0f;
ftype t3 = q1*q2*2.0f;
ftype t4 = t2+t3;
ftype t5 = q0*q0;
ftype t6 = q1*q1;
ftype t7 = q2*q2;
ftype t8 = q3*q3;
ftype t9 = t5+t6-t7-t8;
ftype t10 = q0*q2*2.0f;
ftype t25 = q1*q3*2.0f;
ftype t11 = t10-t25;
ftype t12 = q3*ve*2.0f;
ftype t13 = q0*vn*2.0f;
ftype t26 = q2*vd*2.0f;
ftype t14 = t12+t13-t26;
ftype t15 = q3*vd*2.0f;
ftype t16 = q2*ve*2.0f;
ftype t17 = q1*vn*2.0f;
ftype t18 = t15+t16+t17;
ftype t19 = q0*vd*2.0f;
ftype t20 = q2*vn*2.0f;
ftype t27 = q1*ve*2.0f;
ftype t21 = t19+t20-t27;
ftype t22 = q1*vd*2.0f;
ftype t23 = q0*ve*2.0f;
ftype t28 = q3*vn*2.0f;
ftype t24 = t22+t23-t28;
ftype t29 = P[0][0]*t14;
ftype t30 = P[1][1]*t18;
ftype t31 = P[4][5]*t9;
ftype t32 = P[5][5]*t4;
ftype t33 = P[0][5]*t14;
ftype t34 = P[1][5]*t18;
ftype t35 = P[3][5]*t24;
ftype t79 = P[6][5]*t11;
ftype t80 = P[2][5]*t21;
ftype t36 = t31+t32+t33+t34+t35-t79-t80;
ftype t37 = t4*t36;
ftype t38 = P[4][6]*t9;
ftype t39 = P[5][6]*t4;
ftype t40 = P[0][6]*t14;
ftype t41 = P[1][6]*t18;
ftype t42 = P[3][6]*t24;
ftype t81 = P[6][6]*t11;
ftype t82 = P[2][6]*t21;
ftype t43 = t38+t39+t40+t41+t42-t81-t82;
ftype t44 = P[4][0]*t9;
ftype t45 = P[5][0]*t4;
ftype t46 = P[1][0]*t18;
ftype t47 = P[3][0]*t24;
ftype t84 = P[6][0]*t11;
ftype t85 = P[2][0]*t21;
ftype t48 = t29+t44+t45+t46+t47-t84-t85;
ftype t49 = t14*t48;
ftype t50 = P[4][1]*t9;
ftype t51 = P[5][1]*t4;
ftype t52 = P[0][1]*t14;
ftype t53 = P[3][1]*t24;
ftype t86 = P[6][1]*t11;
ftype t87 = P[2][1]*t21;
ftype t54 = t30+t50+t51+t52+t53-t86-t87;
ftype t55 = t18*t54;
ftype t56 = P[4][2]*t9;
ftype t57 = P[5][2]*t4;
ftype t58 = P[0][2]*t14;
ftype t59 = P[1][2]*t18;
ftype t60 = P[3][2]*t24;
ftype t78 = P[2][2]*t21;
ftype t88 = P[6][2]*t11;
ftype t61 = t56+t57+t58+t59+t60-t78-t88;
ftype t62 = P[4][3]*t9;
ftype t63 = P[5][3]*t4;
ftype t64 = P[0][3]*t14;
ftype t65 = P[1][3]*t18;
ftype t66 = P[3][3]*t24;
ftype t90 = P[6][3]*t11;
ftype t91 = P[2][3]*t21;
ftype t67 = t62+t63+t64+t65+t66-t90-t91;
ftype t68 = t24*t67;
ftype t69 = P[4][4]*t9;
ftype t70 = P[5][4]*t4;
ftype t71 = P[0][4]*t14;
ftype t72 = P[1][4]*t18;
ftype t73 = P[3][4]*t24;
ftype t92 = P[6][4]*t11;
ftype t93 = P[2][4]*t21;
ftype t74 = t69+t70+t71+t72+t73-t92-t93;
ftype t75 = t9*t74;
ftype t83 = t11*t43;
ftype t89 = t21*t61;
ftype t76 = R_VEL+t37+t49+t55+t68+t75-t83-t89;
ftype 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] = t76;
// 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
zero_range(&Kfusion[0], 10, 12);
}
if (!inhibitDelVelBiasStates && !badIMUdata) {
for (uint8_t index = 0; index < 3; index++) {
const uint8_t stateIndex = index + 13;
if (!dvelBiasAxisInhibit[index]) {
Kfusion[stateIndex] = t77*(P[stateIndex][5]*t4+P[stateIndex][4]*t9+P[stateIndex][0]*t14-P[stateIndex][6]*t11+P[stateIndex][1]*t18-P[stateIndex][2]*t21+P[stateIndex][3]*t24);
} else {
Kfusion[stateIndex] = 0.0f;
}
}
} else {
// zero indexes 13 to 15 = 3
zero_range(&Kfusion[0], 13, 15);
}
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
zero_range(&Kfusion[0], 16, 21);
}
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
zero_range(&Kfusion[0], 22, 23);
}
} 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
ftype R_VEL = sq(bodyOdmDataDelayed.velErr);
ftype t2 = q0*q3*2.0f;
ftype t9 = q1*q2*2.0f;
ftype t3 = t2-t9;
ftype t4 = q0*q0;
ftype t5 = q1*q1;
ftype t6 = q2*q2;
ftype t7 = q3*q3;
ftype t8 = t4-t5+t6-t7;
ftype t10 = q0*q1*2.0f;
ftype t11 = q2*q3*2.0f;
ftype t12 = t10+t11;
ftype t13 = q1*vd*2.0f;
ftype t14 = q0*ve*2.0f;
ftype t26 = q3*vn*2.0f;
ftype t15 = t13+t14-t26;
ftype t16 = q0*vd*2.0f;
ftype t17 = q2*vn*2.0f;
ftype t27 = q1*ve*2.0f;
ftype t18 = t16+t17-t27;
ftype t19 = q3*vd*2.0f;
ftype t20 = q2*ve*2.0f;
ftype t21 = q1*vn*2.0f;
ftype t22 = t19+t20+t21;
ftype t23 = q3*ve*2.0f;
ftype t24 = q0*vn*2.0f;
ftype t28 = q2*vd*2.0f;
ftype t25 = t23+t24-t28;
ftype t29 = P[0][0]*t15;
ftype t30 = P[1][1]*t18;
ftype t31 = P[5][4]*t8;
ftype t32 = P[6][4]*t12;
ftype t33 = P[0][4]*t15;
ftype t34 = P[1][4]*t18;
ftype t35 = P[2][4]*t22;
ftype t78 = P[4][4]*t3;
ftype t79 = P[3][4]*t25;
ftype t36 = t31+t32+t33+t34+t35-t78-t79;
ftype t37 = P[5][6]*t8;
ftype t38 = P[6][6]*t12;
ftype t39 = P[0][6]*t15;
ftype t40 = P[1][6]*t18;
ftype t41 = P[2][6]*t22;
ftype t81 = P[4][6]*t3;
ftype t82 = P[3][6]*t25;
ftype t42 = t37+t38+t39+t40+t41-t81-t82;
ftype t43 = t12*t42;
ftype t44 = P[5][0]*t8;
ftype t45 = P[6][0]*t12;
ftype t46 = P[1][0]*t18;
ftype t47 = P[2][0]*t22;
ftype t83 = P[4][0]*t3;
ftype t84 = P[3][0]*t25;
ftype t48 = t29+t44+t45+t46+t47-t83-t84;
ftype t49 = t15*t48;
ftype t50 = P[5][1]*t8;
ftype t51 = P[6][1]*t12;
ftype t52 = P[0][1]*t15;
ftype t53 = P[2][1]*t22;
ftype t85 = P[4][1]*t3;
ftype t86 = P[3][1]*t25;
ftype t54 = t30+t50+t51+t52+t53-t85-t86;
ftype t55 = t18*t54;
ftype t56 = P[5][2]*t8;
ftype t57 = P[6][2]*t12;
ftype t58 = P[0][2]*t15;
ftype t59 = P[1][2]*t18;
ftype t60 = P[2][2]*t22;
ftype t87 = P[4][2]*t3;
ftype t88 = P[3][2]*t25;
ftype t61 = t56+t57+t58+t59+t60-t87-t88;
ftype t62 = t22*t61;
ftype t63 = P[5][3]*t8;
ftype t64 = P[6][3]*t12;
ftype t65 = P[0][3]*t15;
ftype t66 = P[1][3]*t18;
ftype t67 = P[2][3]*t22;
ftype t89 = P[4][3]*t3;
ftype t90 = P[3][3]*t25;
ftype t68 = t63+t64+t65+t66+t67-t89-t90;
ftype t69 = P[5][5]*t8;
ftype t70 = P[6][5]*t12;
ftype t71 = P[0][5]*t15;
ftype t72 = P[1][5]*t18;
ftype t73 = P[2][5]*t22;
ftype t92 = P[4][5]*t3;
ftype t93 = P[3][5]*t25;
ftype t74 = t69+t70+t71+t72+t73-t92-t93;
ftype t75 = t8*t74;
ftype t80 = t3*t36;
ftype t91 = t25*t68;
ftype t76 = R_VEL+t43+t49+t55+t62+t75-t80-t91;
ftype 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] = t76;
// 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
zero_range(&Kfusion[0], 10, 12);
}
if (!inhibitDelVelBiasStates && !badIMUdata) {
for (uint8_t index = 0; index < 3; index++) {
const uint8_t stateIndex = index + 13;
if (!dvelBiasAxisInhibit[index]) {
Kfusion[stateIndex] = t77*(-P[stateIndex][4]*t3+P[stateIndex][5]*t8+P[stateIndex][0]*t15+P[stateIndex][6]*t12+P[stateIndex][1]*t18+P[stateIndex][2]*t22-P[stateIndex][3]*t25);
} else {
Kfusion[stateIndex] = 0.0f;
}
}
} else {
// zero indexes 13 to 15
zero_range(&Kfusion[0], 13, 15);
}
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
zero_range(&Kfusion[0], 16, 21);
}
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
zero_range(&Kfusion[0], 22, 23);
}
} 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
ftype R_VEL = sq(bodyOdmDataDelayed.velErr);
ftype t2 = q0*q2*2.0f;
ftype t3 = q1*q3*2.0f;
ftype t4 = t2+t3;
ftype t5 = q0*q0;
ftype t6 = q1*q1;
ftype t7 = q2*q2;
ftype t8 = q3*q3;
ftype t9 = t5-t6-t7+t8;
ftype t10 = q0*q1*2.0f;
ftype t25 = q2*q3*2.0f;
ftype t11 = t10-t25;
ftype t12 = q0*vd*2.0f;
ftype t13 = q2*vn*2.0f;
ftype t26 = q1*ve*2.0f;
ftype t14 = t12+t13-t26;
ftype t15 = q1*vd*2.0f;
ftype t16 = q0*ve*2.0f;
ftype t27 = q3*vn*2.0f;
ftype t17 = t15+t16-t27;
ftype t18 = q3*ve*2.0f;
ftype t19 = q0*vn*2.0f;
ftype t28 = q2*vd*2.0f;
ftype t20 = t18+t19-t28;
ftype t21 = q3*vd*2.0f;
ftype t22 = q2*ve*2.0f;
ftype t23 = q1*vn*2.0f;
ftype t24 = t21+t22+t23;
ftype t29 = P[0][0]*t14;
ftype t30 = P[6][4]*t9;
ftype t31 = P[4][4]*t4;
ftype t32 = P[0][4]*t14;
ftype t33 = P[2][4]*t20;
ftype t34 = P[3][4]*t24;
ftype t78 = P[5][4]*t11;
ftype t79 = P[1][4]*t17;
ftype t35 = t30+t31+t32+t33+t34-t78-t79;
ftype t36 = t4*t35;
ftype t37 = P[6][5]*t9;
ftype t38 = P[4][5]*t4;
ftype t39 = P[0][5]*t14;
ftype t40 = P[2][5]*t20;
ftype t41 = P[3][5]*t24;
ftype t80 = P[5][5]*t11;
ftype t81 = P[1][5]*t17;
ftype t42 = t37+t38+t39+t40+t41-t80-t81;
ftype t43 = P[6][0]*t9;
ftype t44 = P[4][0]*t4;
ftype t45 = P[2][0]*t20;
ftype t46 = P[3][0]*t24;
ftype t83 = P[5][0]*t11;
ftype t84 = P[1][0]*t17;
ftype t47 = t29+t43+t44+t45+t46-t83-t84;
ftype t48 = t14*t47;
ftype t49 = P[6][1]*t9;
ftype t50 = P[4][1]*t4;
ftype t51 = P[0][1]*t14;
ftype t52 = P[2][1]*t20;
ftype t53 = P[3][1]*t24;
ftype t85 = P[5][1]*t11;
ftype t86 = P[1][1]*t17;
ftype t54 = t49+t50+t51+t52+t53-t85-t86;
ftype t55 = P[6][2]*t9;
ftype t56 = P[4][2]*t4;
ftype t57 = P[0][2]*t14;
ftype t58 = P[2][2]*t20;
ftype t59 = P[3][2]*t24;
ftype t88 = P[5][2]*t11;
ftype t89 = P[1][2]*t17;
ftype t60 = t55+t56+t57+t58+t59-t88-t89;
ftype t61 = t20*t60;
ftype t62 = P[6][3]*t9;
ftype t63 = P[4][3]*t4;
ftype t64 = P[0][3]*t14;
ftype t65 = P[2][3]*t20;
ftype t66 = P[3][3]*t24;
ftype t90 = P[5][3]*t11;
ftype t91 = P[1][3]*t17;
ftype t67 = t62+t63+t64+t65+t66-t90-t91;
ftype t68 = t24*t67;
ftype t69 = P[6][6]*t9;
ftype t70 = P[4][6]*t4;
ftype t71 = P[0][6]*t14;
ftype t72 = P[2][6]*t20;
ftype t73 = P[3][6]*t24;
ftype t92 = P[5][6]*t11;
ftype t93 = P[1][6]*t17;
ftype t74 = t69+t70+t71+t72+t73-t92-t93;
ftype t75 = t9*t74;
ftype t82 = t11*t42;
ftype t87 = t17*t54;
ftype t76 = R_VEL+t36+t48+t61+t68+t75-t82-t87;
ftype 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] = t76;
// 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
zero_range(&Kfusion[0], 10, 12);
}
if (!inhibitDelVelBiasStates && !badIMUdata) {
for (uint8_t index = 0; index < 3; index++) {
const uint8_t stateIndex = index + 13;
if (!dvelBiasAxisInhibit[index]) {
Kfusion[stateIndex] = t77*(P[stateIndex][4]*t4+P[stateIndex][0]*t14+P[stateIndex][6]*t9-P[stateIndex][5]*t11-P[stateIndex][1]*t17+P[stateIndex][2]*t20+P[stateIndex][3]*t24);
} else {
Kfusion[stateIndex] = 0.0f;
}
}
} else {
// zero indexes 13 to 15
zero_range(&Kfusion[0], 13, 15);
}
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
zero_range(&Kfusion[0], 16, 21);
}
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
zero_range(&Kfusion[0], 22, 23);
}
} 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;
}
}
}
}
}
#endif // EK3_FEATURE_BODY_ODOM
#if EK3_FEATURE_BODY_ODOM
// 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 body odometry data (aka visual position delta) at the fusion time horizon
const bool bodyOdomDataToFuse = storedBodyOdm.recall(bodyOdmDataDelayed, imuDataDelayed.time_ms);
if (bodyOdomDataToFuse && frontend->sources.useVelXYSource(AP_NavEKF_Source::SourceXY::EXTNAV)) {
// Fuse data into the main filter
FuseBodyVel();
}
// Check for wheel encoder data at the fusion time horizon
const bool wheelOdomDataToFuse = storedWheelOdm.recall(wheelOdmDataDelayed, imuDataDelayed.time_ms);
if (wheelOdomDataToFuse && frontend->sources.useVelXYSource(AP_NavEKF_Source::SourceXY::WHEEL_ENCODER)) {
// check if the delta time is too small to calculate a velocity
if (wheelOdmDataDelayed.delTime > EKF_TARGET_DT) {
// get the forward velocity
ftype 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
bodyOdmDataDelayed.vel = prevTnb * velNED;
bodyOdmDataDelayed.body_offset = wheelOdmDataDelayed.hub_offset;
bodyOdmDataDelayed.velErr = frontend->_wencOdmVelErr;
// Fuse data into the main filter
FuseBodyVel();
}
}
}
#endif // EK3_FEATURE_BODY_ODOM