// resets position states to last GPS measurement or to zero if in constant position mode
void NavEKF2_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,6,7);
zeroCols(P,6,7);
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[6][6] = P[7][7] = sq(frontend->_gpsHorizPosNoise);
} else {
// Use GPS data as first preference if fresh data is available
if (imuSampleTime_ms - lastTimeGpsReceived_ms < 250) {
// 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[6][6] = P[7][7] = sq(MAX(gpsPosAccuracy,frontend->_gpsHorizPosNoise));
// clear the timeout flags and counters
posTimeout = false;
lastPosPassTime_ms = imuSampleTime_ms;
} else if (imuSampleTime_ms - rngBcnLast3DmeasTime_ms < 250) {
// 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[6][6] = receiverPosCov[0][0];
P[7][7] = 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;
}
// reset the body axis gyro bias states to zero and re-initialise the corresponding covariances
// Assume that the calibration is performed to an accuracy of 0.5 deg/sec which will require averaging under static conditions
// WARNING - a non-blocking calibration method must be used
void NavEKF2_core::resetGyroBias(void)
{
stateStruct.gyro_bias.zero();
zeroRows(P,9,11);
zeroCols(P,9,11);
P[9][9] = sq(radians(0.5f * dtIMUavg));
P[10][10] = P[9][9];
P[11][11] = P[9][9];
}
// Reset heading and magnetic field states
bool Ekf::resetMagHeading(Vector3f &mag_init)
{
// If we don't a tilt estimate then we cannot initialise the yaw
if (!_control_status.flags.tilt_align) {
return false;
}
// get the roll, pitch, yaw estimates and set the yaw to zero
matrix::Quaternion<float> q(_state.quat_nominal(0), _state.quat_nominal(1), _state.quat_nominal(2),
_state.quat_nominal(3));
matrix::Euler<float> euler_init(q);
euler_init(2) = 0.0f;
// rotate the magnetometer measurements into earth axes
matrix::Dcm<float> R_to_earth_zeroyaw(euler_init);
Vector3f mag_ef_zeroyaw = R_to_earth_zeroyaw * mag_init;
euler_init(2) = _mag_declination - atan2f(mag_ef_zeroyaw(1), mag_ef_zeroyaw(0));
// calculate initial quaternion states for the ekf
// we don't change the output attitude to avoid jumps
_state.quat_nominal = Quaternion(euler_init);
// reset the angle error variances because the yaw angle could have changed by a significant amount
// by setting them to zero we avoid 'kicks' in angle when 3-D fusion starts and the imu process noise
// will grow them again.
zeroRows(P, 0, 2);
zeroCols(P, 0, 2);
// calculate initial earth magnetic field states
matrix::Dcm<float> R_to_earth(euler_init);
_state.mag_I = R_to_earth * mag_init;
// reset the corresponding rows and columns in the covariance matrix and set the variances on the magnetic field states to the measurement variance
zeroRows(P, 16, 21);
zeroCols(P, 16, 21);
for (uint8_t index = 16; index <= 21; index ++) {
P[index][index] = sq(_params.mag_noise);
}
return true;
}
// 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 NavEKF2_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,3,4);
zeroCols(P,3,4);
if (PV_AidingMode != AID_ABSOLUTE) {
stateStruct.velocity.zero();
// set the variances using the measurement noise parameter
P[4][4] = P[3][3] = sq(frontend->_gpsHorizVelNoise);
} else {
// reset horizontal velocity states to the GPS velocity if available
if (imuSampleTime_ms - lastTimeGpsReceived_ms < 250) {
stateStruct.velocity.x = gpsDataNew.vel.x;
stateStruct.velocity.y = gpsDataNew.vel.y;
// set the variances using the reported GPS speed accuracy
P[4][4] = P[3][3] = 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[4][4] = P[3][3] = 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;
}
// reset the vertical position state using the last height measurement
void NavEKF2_core::ResetHeight(void)
{
// write to the state vector
stateStruct.position.z = -hgtMea;
terrainState = stateStruct.position.z + rngOnGnd;
for (uint8_t i=0; i<imu_buffer_length; i++) {
storedOutput[i].position.z = stateStruct.position.z;
}
outputDataNew.position.z = stateStruct.position.z;
outputDataDelayed.position.z = stateStruct.position.z;
// reset the corresponding covariances
zeroRows(P,8,8);
zeroCols(P,8,8);
// set the variances to the measurement variance
P[8][8] = 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) {
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,5,5);
zeroCols(P,5,5);
// set the variances to the measurement variance
P[5][5] = sq(frontend->_gpsVertVelNoise);
}
// resets position states to last GPS measurement or to zero if in constant position mode
void NavEKF2_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;
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;
} else {
// 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));
}
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;
// reset the corresponding covariances
zeroRows(P,6,7);
zeroCols(P,6,7);
// set the variances to the measurement variance
P[6][6] = P[7][7] = sq(frontend->_gpsHorizPosNoise);
}
// 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 NavEKF2_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;
if (PV_AidingMode != AID_ABSOLUTE) {
stateStruct.velocity.zero();
} else {
// reset horizontal velocity states to the GPS velocity
stateStruct.velocity.x = gpsDataNew.vel.x; // north velocity from blended accel data
stateStruct.velocity.y = gpsDataNew.vel.y; // east velocity from blended accel data
}
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;
// reset the corresponding covariances
zeroRows(P,3,4);
zeroCols(P,3,4);
// set the variances to the measurement variance
P[4][4] = P[3][3] = sq(frontend->_gpsHorizVelNoise);
}
// fuse selected position, velocity and height measurements
void NavEKF2_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
float posErr;
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) {
// set the GPS data timeout depending on whether airspeed data is present
uint32_t gpsRetryTime;
if (useAirspeed()) gpsRetryTime = frontend->gpsRetryTimeUseTAS_ms;
else gpsRetryTime = frontend->gpsRetryTimeNoTAS_ms;
// 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
posErr = frontend->gpsPosVarAccScale * accNavMag;
// estimate the GPS Velocity, GPS horiz position and height measurement variances.
// if the GPS is able to report a speed error, we use it to adjust the observation noise for GPS velocity
// otherwise we scale it using manoeuvre acceleration
// Use different errors if flying without GPS using synthetic position and velocity data
if (PV_AidingMode == AID_NONE && inFlight) {
// Assume the vehicle will be flown with velocity changes less than 10 m/s in this mode (realistic for indoor use)
// This is a compromise between corrections for gyro errors and reducing angular errors due to maneouvres
R_OBS[0] = sq(10.0f);
R_OBS[1] = R_OBS[0];
R_OBS[2] = R_OBS[0];
// Assume a large position uncertainty so as to contrain position states in this mode but minimise angular errors due to manoeuvres
R_OBS[3] = sq(25.0f);
R_OBS[4] = R_OBS[3];
} else {
if (gpsSpdAccuracy > 0.0f) {
// use GPS receivers reported speed accuracy if available and floor at value set by gps 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];
R_OBS[3] = sq(constrain_float(frontend->_gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
R_OBS[4] = R_OBS[3];
}
R_OBS[5] = posDownObsNoise;
// 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 perfomrance
// plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
for (uint8_t i=0; i<=1; i++) R_OBS_DATA_CHECKS[i] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
for (uint8_t i=2; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
// if vertical GPS velocity data and an independant 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[8][8] + R_OBS_DATA_CHECKS[5])) && (sq(velDErr) > 9.0f * (P[5][5] + 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[6][6] + R_OBS_DATA_CHECKS[3];
varInnovVelPos[4] = P[7][7] + 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);
// declare a timeout condition if we have been too long without data or not aiding
posTimeout = (((imuSampleTime_ms - lastPosPassTime_ms) > gpsRetryTime) || PV_AidingMode == AID_NONE);
// use position data if healthy, timed out, or in constant position mode
if (posHealth || posTimeout || (PV_AidingMode == AID_NONE)) {
posHealth = true;
// only reset the failed time and do glitch timeout checks if we are doing full aiding
if (PV_AidingMode == AID_ABSOLUTE) {
lastPosPassTime_ms = imuSampleTime_ms;
// if timed out or outside the specified uncertainty radius, reset to the GPS
if (posTimeout || ((P[6][6] + 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,6,7);
zeroCols(P,6,7);
P[6][6] = sq(float(0.5f*frontend->_gpsGlitchRadiusMax));
P[7][7] = P[6][6];
// Reset the normalised innovation to avoid failing the bad fusion tests
posTestRatio = 0.0f;
velTestRatio = 0.0f;
}
}
} else {
posHealth = false;
}
}
// test velocity measurements
if (fuseVelData) {
// test velocity measurements
uint8_t imax = 2;
// Don't fuse vertical velocity observations if inhibited by the user or if we are using synthetic data
if (frontend->_fusionModeGPS >= 1 || PV_AidingMode != AID_ABSOLUTE) {
imax = 1;
}
float innovVelSumSq = 0; // sum of squares of velocity innovations
float varVelSum = 0; // sum of velocity innovation variances
for (uint8_t i = 0; i<=imax; i++) {
// velocity states start at index 3
stateIndex = i + 3;
// 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);
// declare a timeout if we have not fused velocity data for too long or not aiding
velTimeout = (((imuSampleTime_ms - lastVelPassTime_ms) > gpsRetryTime) || PV_AidingMode == AID_NONE);
// 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[8][8] + 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();
hgtTimeout = false;
}
// 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;
}
tiltErrVec.zero();
}
if (fusePosData && posHealth) {
fuseData[3] = true;
fuseData[4] = true;
tiltErrVec.zero();
}
if (fuseHgtData && hgtHealth) {
fuseData[5] = true;
}
// fuse measurements sequentially
for (obsIndex=0; obsIndex<=5; obsIndex++) {
if (fuseData[obsIndex]) {
stateIndex = 3 + obsIndex;
// calculate the measurement innovation, using states from a different time coordinate if fusing height data
// adjust scaling on GPS measurement noise variances if not enough satellites
if (obsIndex <= 2)
{
innovVelPos[obsIndex] = stateStruct.velocity[obsIndex] - observation[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
}
else if (obsIndex == 3 || obsIndex == 4) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
R_OBS[obsIndex] *= sq(gpsNoiseScaler);
} else if (obsIndex == 5) {
innovVelPos[obsIndex] = stateStruct.position[obsIndex-3] - observation[obsIndex];
const float gndMaxBaroErr = 4.0f;
const float gndBaroInnovFloor = -0.5f;
if(getTouchdownExpected()) {
// when a touchdown is expected, floor the barometer innovation at gndBaroInnovFloor
// constrain the correction between 0 and gndBaroInnovFloor+gndMaxBaroErr
// this function looks like this:
// |/
//---------|---------
// ____/|
// / |
// / |
innovVelPos[5] += constrain_float(-innovVelPos[5]+gndBaroInnovFloor, 0.0f, gndBaroInnovFloor+gndMaxBaroErr);
}
}
// calculate the Kalman gain and calculate innovation variances
varInnovVelPos[obsIndex] = P[stateIndex][stateIndex] + R_OBS[obsIndex];
SK = 1.0f/varInnovVelPos[obsIndex];
for (uint8_t i= 0; i<=15; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
// inhibit magnetic field state estimation by setting Kalman gains to zero
if (!inhibitMagStates) {
for (uint8_t i = 16; i<=21; i++) {
Kfusion[i] = P[i][stateIndex]*SK;
}
} else {
for (uint8_t i = 16; i<=21; i++) {
Kfusion[i] = 0.0f;
}
}
// inhibit wind state estimation by setting Kalman gains to zero
if (!inhibitWindStates) {
Kfusion[22] = P[22][stateIndex]*SK;
Kfusion[23] = P[23][stateIndex]*SK;
} else {
Kfusion[22] = 0.0f;
Kfusion[23] = 0.0f;
}
// zero the attitude error state - by definition it is assumed to be zero before each observaton fusion
stateStruct.angErr.zero();
// calculate state corrections and re-normalise the quaternions for states predicted using the blended IMU data
for (uint8_t i = 0; i<=stateIndexLim; i++) {
statesArray[i] = statesArray[i] - Kfusion[i] * innovVelPos[obsIndex];
}
// the first 3 states represent the angular misalignment vector. This is
// is used to correct the estimated quaternion
stateStruct.quat.rotate(stateStruct.angErr);
// sum the attitude error from velocity and position fusion only
// used as a metric for convergence monitoring
if (obsIndex != 5) {
tiltErrVec += stateStruct.angErr;
}
// 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];
}
}
for (uint8_t i= 0; i<=stateIndexLim; i++) {
for (uint8_t j= 0; j<=stateIndexLim; j++) {
P[i][j] = P[i][j] - KHP[i][j];
}
}
}
}
}
// force the covariance matrix to be symmetrical and limit the variances to prevent ill-condiioning.
ForceSymmetry();
ConstrainVariances();
// stop performance timer
hal.util->perf_end(_perf_FuseVelPosNED);
}
void Ekf::controlOpticalFlowFusion()
{
// Check for new optical flow data that has fallen behind the fusion time horizon
if (_flow_data_ready) {
// optical flow fusion mode selection logic
if ((_params.fusion_mode & MASK_USE_OF) // optical flow has been selected by the user
&& !_control_status.flags.opt_flow // we are not yet using flow data
&& _control_status.flags.tilt_align // we know our tilt attitude
&& (_time_last_imu - _time_last_hagl_fuse) < 5e5) // we have a valid distance to ground estimate
{
// If the heading is not aligned, reset the yaw and magnetic field states
if (!_control_status.flags.yaw_align) {
_control_status.flags.yaw_align = resetMagHeading(_mag_sample_delayed.mag);
}
// If the heading is valid, start using optical flow aiding
if (_control_status.flags.yaw_align) {
// set the flag and reset the fusion timeout
_control_status.flags.opt_flow = true;
_time_last_of_fuse = _time_last_imu;
// if we are not using GPS then the velocity and position states and covariances need to be set
if (!_control_status.flags.gps) {
// constrain height above ground to be above minimum possible
float heightAboveGndEst = fmaxf((_terrain_vpos - _state.pos(2)), _params.rng_gnd_clearance);
// calculate absolute distance from focal point to centre of frame assuming a flat earth
float range = heightAboveGndEst / _R_to_earth(2, 2);
if ((range - _params.rng_gnd_clearance) > 0.3f && _flow_sample_delayed.dt > 0.05f) {
// we should have reliable OF measurements so
// calculate X and Y body relative velocities from OF measurements
Vector3f vel_optflow_body;
vel_optflow_body(0) = - range * _flow_sample_delayed.flowRadXYcomp(1) / _flow_sample_delayed.dt;
vel_optflow_body(1) = range * _flow_sample_delayed.flowRadXYcomp(0) / _flow_sample_delayed.dt;
vel_optflow_body(2) = 0.0f;
// rotate from body to earth frame
Vector3f vel_optflow_earth;
vel_optflow_earth = _R_to_earth * vel_optflow_body;
// take x and Y components
_state.vel(0) = vel_optflow_earth(0);
_state.vel(1) = vel_optflow_earth(1);
} else {
_state.vel(0) = 0.0f;
_state.vel(1) = 0.0f;
}
// reset the velocity covariance terms
zeroRows(P,4,5);
zeroCols(P,4,5);
// reset the horizontal velocity variance using the optical flow noise variance
P[5][5] = P[4][4] = sq(range) * calcOptFlowMeasVar();
if (!_control_status.flags.in_air) {
// we are likely starting OF for the first time so reset the horizontal position and vertical velocity states
_state.pos(0) = 0.0f;
_state.pos(1) = 0.0f;
// reset the corresponding covariances
// we are by definition at the origin at commencement so variances are also zeroed
zeroRows(P,7,8);
zeroCols(P,7,8);
// align the output observer to the EKF states
alignOutputFilter();
}
}
}
} else if (!(_params.fusion_mode & MASK_USE_OF)) {
_control_status.flags.opt_flow = false;
}
// handle the case when we are relying on optical flow fusion and lose it
if (_control_status.flags.opt_flow && !_control_status.flags.gps) {
// We are relying on flow aiding to constrain attitude drift so after 5s without aiding we need to do something
if ((_time_last_imu - _time_last_of_fuse > 5e6)) {
// Switch to the non-aiding mode, zero the velocity states
// and set the synthetic position to the current estimate
_control_status.flags.opt_flow = false;
_last_known_posNE(0) = _state.pos(0);
_last_known_posNE(1) = _state.pos(1);
_state.vel.setZero();
}
}
// fuse the data
if (_control_status.flags.opt_flow) {
// Update optical flow bias estimates
calcOptFlowBias();
// Fuse optical flow LOS rate observations into the main filter
fuseOptFlow();
_last_known_posNE(0) = _state.pos(0);
_last_known_posNE(1) = _state.pos(1);
}
}
}
// initialise the quaternion covariances using rotation vector variances
void Ekf::initialiseQuatCovariances(Vector3f &rot_vec_var)
{
// calculate an equivalent rotation vector from the quaternion
float q0,q1,q2,q3;
if (_state.quat_nominal(0) >= 0.0f) {
q0 = _state.quat_nominal(0);
q1 = _state.quat_nominal(1);
q2 = _state.quat_nominal(2);
q3 = _state.quat_nominal(3);
} else {
q0 = -_state.quat_nominal(0);
q1 = -_state.quat_nominal(1);
q2 = -_state.quat_nominal(2);
q3 = -_state.quat_nominal(3);
}
float delta = 2.0f*acosf(q0);
float scaler = (delta/sinf(delta*0.5f));
float rotX = scaler*q1;
float rotY = scaler*q2;
float rotZ = scaler*q3;
// autocode generated using matlab symbolic toolbox
float t2 = rotX*rotX;
float t4 = rotY*rotY;
float t5 = rotZ*rotZ;
float t6 = t2+t4+t5;
if (t6 > 1e-9f) {
float t7 = sqrtf(t6);
float t8 = t7*0.5f;
float t3 = sinf(t8);
float t9 = t3*t3;
float t10 = 1.0f/t6;
float t11 = 1.0f/sqrtf(t6);
float t12 = cosf(t8);
float t13 = 1.0f/powf(t6,1.5f);
float t14 = t3*t11;
float t15 = rotX*rotY*t3*t13;
float t16 = rotX*rotZ*t3*t13;
float t17 = rotY*rotZ*t3*t13;
float t18 = t2*t10*t12*0.5f;
float t27 = t2*t3*t13;
float t19 = t14+t18-t27;
float t23 = rotX*rotY*t10*t12*0.5f;
float t28 = t15-t23;
float t20 = rotY*rot_vec_var(1)*t3*t11*t28*0.5f;
float t25 = rotX*rotZ*t10*t12*0.5f;
float t31 = t16-t25;
float t21 = rotZ*rot_vec_var(2)*t3*t11*t31*0.5f;
float t22 = t20+t21-rotX*rot_vec_var(0)*t3*t11*t19*0.5f;
float t24 = t15-t23;
float t26 = t16-t25;
float t29 = t4*t10*t12*0.5f;
float t34 = t3*t4*t13;
float t30 = t14+t29-t34;
float t32 = t5*t10*t12*0.5f;
float t40 = t3*t5*t13;
float t33 = t14+t32-t40;
float t36 = rotY*rotZ*t10*t12*0.5f;
float t39 = t17-t36;
float t35 = rotZ*rot_vec_var(2)*t3*t11*t39*0.5f;
float t37 = t15-t23;
float t38 = t17-t36;
float t41 = rot_vec_var(0)*(t15-t23)*(t16-t25);
float t42 = t41-rot_vec_var(1)*t30*t39-rot_vec_var(2)*t33*t39;
float t43 = t16-t25;
float t44 = t17-t36;
// zero all the quaternion covariances
zeroRows(P,0,3);
zeroCols(P,0,3);
// Update the quaternion internal covariances using auto-code generated using matlab symbolic toolbox
P[0][0] = rot_vec_var(0)*t2*t9*t10*0.25f+rot_vec_var(1)*t4*t9*t10*0.25f+rot_vec_var(2)*t5*t9*t10*0.25f;
P[0][1] = t22;
P[0][2] = t35+rotX*rot_vec_var(0)*t3*t11*(t15-rotX*rotY*t10*t12*0.5f)*0.5f-rotY*rot_vec_var(1)*t3*t11*t30*0.5f;
P[0][3] = rotX*rot_vec_var(0)*t3*t11*(t16-rotX*rotZ*t10*t12*0.5f)*0.5f+rotY*rot_vec_var(1)*t3*t11*(t17-rotY*rotZ*t10*t12*0.5f)*0.5f-rotZ*rot_vec_var(2)*t3*t11*t33*0.5f;
P[1][0] = t22;
P[1][1] = rot_vec_var(0)*(t19*t19)+rot_vec_var(1)*(t24*t24)+rot_vec_var(2)*(t26*t26);
P[1][2] = rot_vec_var(2)*(t16-t25)*(t17-rotY*rotZ*t10*t12*0.5f)-rot_vec_var(0)*t19*t28-rot_vec_var(1)*t28*t30;
P[1][3] = rot_vec_var(1)*(t15-t23)*(t17-rotY*rotZ*t10*t12*0.5f)-rot_vec_var(0)*t19*t31-rot_vec_var(2)*t31*t33;
P[2][0] = t35-rotY*rot_vec_var(1)*t3*t11*t30*0.5f+rotX*rot_vec_var(0)*t3*t11*(t15-t23)*0.5f;
P[2][1] = rot_vec_var(2)*(t16-t25)*(t17-t36)-rot_vec_var(0)*t19*t28-rot_vec_var(1)*t28*t30;
P[2][2] = rot_vec_var(1)*(t30*t30)+rot_vec_var(0)*(t37*t37)+rot_vec_var(2)*(t38*t38);
P[2][3] = t42;
P[3][0] = rotZ*rot_vec_var(2)*t3*t11*t33*(-0.5f)+rotX*rot_vec_var(0)*t3*t11*(t16-t25)*0.5f+rotY*rot_vec_var(1)*t3*t11*(t17-t36)*0.5f;
P[3][1] = rot_vec_var(1)*(t15-t23)*(t17-t36)-rot_vec_var(0)*t19*t31-rot_vec_var(2)*t31*t33;
P[3][2] = t42;
P[3][3] = rot_vec_var(2)*(t33*t33)+rot_vec_var(0)*(t43*t43)+rot_vec_var(1)*(t44*t44);
} else {
// the equations are badly conditioned so use a small angle approximation
P[0][0] = 0.0f;
P[0][1] = 0.0f;
P[0][2] = 0.0f;
P[0][3] = 0.0f;
P[1][0] = 0.0f;
P[1][1] = 0.25f*rot_vec_var(0);
P[1][2] = 0.0f;
P[1][3] = 0.0f;
P[2][0] = 0.0f;
P[2][1] = 0.0f;
P[2][2] = 0.25f*rot_vec_var(1);
P[2][3] = 0.0f;
P[3][0] = 0.0f;
P[3][1] = 0.0f;
P[3][2] = 0.0f;
P[3][3] = 0.25f*rot_vec_var(2);
}
}
// Reset heading and magnetic field states
bool Ekf::resetMagHeading(Vector3f &mag_init)
{
// save a copy of the quaternion state for later use in calculating the amount of reset change
Quaternion quat_before_reset = _state.quat_nominal;
// calculate the variance for the rotation estimate expressed as a rotation vector
// this will be used later to reset the quaternion state covariances
Vector3f angle_err_var_vec = calcRotVecVariances();
// update transformation matrix from body to world frame using the current estimate
_R_to_earth = quat_to_invrotmat(_state.quat_nominal);
// calculate the initial quaternion
// determine if a 321 or 312 Euler sequence is best
if (fabsf(_R_to_earth(2, 0)) < fabsf(_R_to_earth(2, 1))) {
// use a 321 sequence
// rotate the magnetometer measurement into earth frame
matrix::Euler<float> euler321(_state.quat_nominal);
// Set the yaw angle to zero and calculate the rotation matrix from body to earth frame
euler321(2) = 0.0f;
matrix::Dcm<float> R_to_earth(euler321);
// calculate the observed yaw angle
if (_params.fusion_mode & MASK_USE_EVYAW) {
// convert the observed quaternion to a rotation matrix
matrix::Dcm<float> R_to_earth_ev(_ev_sample_delayed.quat); // transformation matrix from body to world frame
// calculate the yaw angle for a 312 sequence
euler321(2) = atan2f(R_to_earth_ev(1, 0) , R_to_earth_ev(0, 0));
} else if (_params.mag_fusion_type <= MAG_FUSE_TYPE_3D) {
// rotate the magnetometer measurements into earth frame using a zero yaw angle
Vector3f mag_earth_pred = R_to_earth * _mag_sample_delayed.mag;
// the angle of the projection onto the horizontal gives the yaw angle
euler321(2) = -atan2f(mag_earth_pred(1), mag_earth_pred(0)) + _mag_declination;
} else {
// there is no yaw observation
return false;
}
// calculate initial quaternion states for the ekf
// we don't change the output attitude to avoid jumps
_state.quat_nominal = Quaternion(euler321);
} else {
// use a 312 sequence
// Calculate the 312 sequence euler angles that rotate from earth to body frame
// See http://www.atacolorado.com/eulersequences.doc
Vector3f euler312;
euler312(0) = atan2f(-_R_to_earth(0, 1) , _R_to_earth(1, 1)); // first rotation (yaw)
euler312(1) = asinf(_R_to_earth(2, 1)); // second rotation (roll)
euler312(2) = atan2f(-_R_to_earth(2, 0) , _R_to_earth(2, 2)); // third rotation (pitch)
// Set the first rotation (yaw) to zero and calculate the rotation matrix from body to earth frame
euler312(0) = 0.0f;
// Calculate the body to earth frame rotation matrix from the euler angles using a 312 rotation sequence
float c2 = cosf(euler312(2));
float s2 = sinf(euler312(2));
float s1 = sinf(euler312(1));
float c1 = cosf(euler312(1));
float s0 = sinf(euler312(0));
float c0 = cosf(euler312(0));
matrix::Dcm<float> R_to_earth;
R_to_earth(0, 0) = c0 * c2 - s0 * s1 * s2;
R_to_earth(1, 1) = c0 * c1;
R_to_earth(2, 2) = c2 * c1;
R_to_earth(0, 1) = -c1 * s0;
R_to_earth(0, 2) = s2 * c0 + c2 * s1 * s0;
R_to_earth(1, 0) = c2 * s0 + s2 * s1 * c0;
R_to_earth(1, 2) = s0 * s2 - s1 * c0 * c2;
R_to_earth(2, 0) = -s2 * c1;
R_to_earth(2, 1) = s1;
// calculate the observed yaw angle
if (_params.fusion_mode & MASK_USE_EVYAW) {
// convert the observed quaternion to a rotation matrix
matrix::Dcm<float> R_to_earth_ev(_ev_sample_delayed.quat); // transformation matrix from body to world frame
// calculate the yaw angle for a 312 sequence
euler312(0) = atan2f(-R_to_earth_ev(0, 1) , R_to_earth_ev(1, 1));
} else if (_params.mag_fusion_type <= MAG_FUSE_TYPE_3D) {
// rotate the magnetometer measurements into earth frame using a zero yaw angle
Vector3f mag_earth_pred = R_to_earth * _mag_sample_delayed.mag;
// the angle of the projection onto the horizontal gives the yaw angle
euler312(0) = -atan2f(mag_earth_pred(1), mag_earth_pred(0)) + _mag_declination;
} else {
// there is no yaw observation
return false;
}
// re-calculate the rotation matrix using the updated yaw angle
s0 = sinf(euler312(0));
c0 = cosf(euler312(0));
R_to_earth(0, 0) = c0 * c2 - s0 * s1 * s2;
R_to_earth(1, 1) = c0 * c1;
R_to_earth(2, 2) = c2 * c1;
R_to_earth(0, 1) = -c1 * s0;
R_to_earth(0, 2) = s2 * c0 + c2 * s1 * s0;
R_to_earth(1, 0) = c2 * s0 + s2 * s1 * c0;
R_to_earth(1, 2) = s0 * s2 - s1 * c0 * c2;
R_to_earth(2, 0) = -s2 * c1;
R_to_earth(2, 1) = s1;
// calculate initial quaternion states for the ekf
// we don't change the output attitude to avoid jumps
_state.quat_nominal = Quaternion(R_to_earth);
}
// update transformation matrix from body to world frame using the current estimate
_R_to_earth = quat_to_invrotmat(_state.quat_nominal);
// update the yaw angle variance using the variance of the measurement
if (_params.fusion_mode & MASK_USE_EVYAW) {
// using error estimate from external vision data
angle_err_var_vec(2) = sq(fmaxf(_ev_sample_delayed.angErr, 1.0e-2f));
} else if (_params.mag_fusion_type <= MAG_FUSE_TYPE_3D) {
// using magnetic heading tuning parameter
angle_err_var_vec(2) = sq(fmaxf(_params.mag_heading_noise, 1.0e-2f));
}
// reset the quaternion covariances using the rotation vector variances
initialiseQuatCovariances(angle_err_var_vec);
// calculate initial earth magnetic field states
_state.mag_I = _R_to_earth * mag_init;
// reset the corresponding rows and columns in the covariance matrix and set the variances on the magnetic field states to the measurement variance
zeroRows(P, 16, 21);
zeroCols(P, 16, 21);
for (uint8_t index = 16; index <= 21; index ++) {
P[index][index] = sq(_params.mag_noise);
}
// calculate the amount that the quaternion has changed by
_state_reset_status.quat_change = _state.quat_nominal * quat_before_reset.inversed();
// add the reset amount to the output observer buffered data
outputSample output_states;
unsigned output_length = _output_buffer.get_length();
for (unsigned i=0; i < output_length; i++) {
output_states = _output_buffer.get_from_index(i);
output_states.quat_nominal *= _state_reset_status.quat_change;
_output_buffer.push_to_index(i,output_states);
}
// apply the change in attitude quaternion to our newest quaternion estimate
// which was already taken out from the output buffer
_output_new.quat_nominal *= _state_reset_status.quat_change;
// capture the reset event
_state_reset_status.quat_counter++;
return true;
}
// Reset height state using the last height measurement
void Ekf::resetHeight()
{
// Get the most recent GPS data
gpsSample gps_newest = _gps_buffer.get_newest();
// store the current vertical position and velocity for reference so we can calculate and publish the reset amount
float old_vert_pos = _state.pos(2);
bool vert_pos_reset = false;
float old_vert_vel = _state.vel(2);
bool vert_vel_reset = false;
// reset the vertical position
if (_control_status.flags.rng_hgt) {
rangeSample range_newest = _range_buffer.get_newest();
if (_time_last_imu - range_newest.time_us < 2 * RNG_MAX_INTERVAL) {
// calculate the new vertical position using range sensor
float new_pos_down = _hgt_sensor_offset - range_newest.rng;
// update the state and assoicated variance
_state.pos(2) = new_pos_down;
// reset the associated covariance values
zeroRows(P, 9, 9);
zeroCols(P, 9, 9);
// the state variance is the same as the observation
P[9][9] = sq(_params.range_noise);
vert_pos_reset = true;
// reset the baro offset which is subtracted from the baro reading if we need to use it as a backup
baroSample baro_newest = _baro_buffer.get_newest();
_baro_hgt_offset = baro_newest.hgt + _state.pos(2);
} else {
// TODO: reset to last known range based estimate
}
} else if (_control_status.flags.baro_hgt) {
// initialize vertical position with newest baro measurement
baroSample baro_newest = _baro_buffer.get_newest();
if (_time_last_imu - baro_newest.time_us < 2 * BARO_MAX_INTERVAL) {
_state.pos(2) = _hgt_sensor_offset - baro_newest.hgt + _baro_hgt_offset;
// reset the associated covariance values
zeroRows(P, 9, 9);
zeroCols(P, 9, 9);
// the state variance is the same as the observation
P[9][9] = sq(_params.baro_noise);
vert_pos_reset = true;
} else {
// TODO: reset to last known baro based estimate
}
} else if (_control_status.flags.gps_hgt) {
// initialize vertical position and velocity with newest gps measurement
if (_time_last_imu - gps_newest.time_us < 2 * GPS_MAX_INTERVAL) {
_state.pos(2) = _hgt_sensor_offset - gps_newest.hgt + _gps_alt_ref;
// reset the associated covarince values
zeroRows(P, 9, 9);
zeroCols(P, 9, 9);
// the state variance is the same as the observation
P[9][9] = sq(gps_newest.hacc);
vert_pos_reset = true;
// reset the baro offset which is subtracted from the baro reading if we need to use it as a backup
baroSample baro_newest = _baro_buffer.get_newest();
_baro_hgt_offset = baro_newest.hgt + _state.pos(2);
} else {
// TODO: reset to last known gps based estimate
}
} else if (_control_status.flags.ev_hgt) {
// initialize vertical position with newest measurement
extVisionSample ev_newest = _ext_vision_buffer.get_newest();
// use the most recent data if it's time offset from the fusion time horizon is smaller
int32_t dt_newest = ev_newest.time_us - _imu_sample_delayed.time_us;
int32_t dt_delayed = _ev_sample_delayed.time_us - _imu_sample_delayed.time_us;
if (abs(dt_newest) < abs(dt_delayed)) {
_state.pos(2) = ev_newest.posNED(2);
} else {
_state.pos(2) = _ev_sample_delayed.posNED(2);
}
}
// reset the vertical velocity covariance values
zeroRows(P, 6, 6);
zeroCols(P, 6, 6);
// reset the vertical velocity state
if (_control_status.flags.gps && (_time_last_imu - gps_newest.time_us < 2 * GPS_MAX_INTERVAL)) {
// If we are using GPS, then use it to reset the vertical velocity
_state.vel(2) = gps_newest.vel(2);
// the state variance is the same as the observation
P[6][6] = sq(1.5f * gps_newest.sacc);
} else {
// we don't know what the vertical velocity is, so set it to zero
_state.vel(2) = 0.0f;
// Set the variance to a value large enough to allow the state to converge quickly
// that does not destabilise the filter
P[6][6] = 10.0f;
}
vert_vel_reset = true;
// store the reset amount and time to be published
if (vert_pos_reset) {
_state_reset_status.posD_change = _state.pos(2) - old_vert_pos;
_state_reset_status.posD_counter++;
}
if (vert_vel_reset) {
_state_reset_status.velD_change = _state.vel(2) - old_vert_vel;
_state_reset_status.velD_counter++;
}
// apply the change in height / height rate to our newest height / height rate estimate
// which have already been taken out from the output buffer
if (vert_pos_reset) {
_output_new.pos(2) += _state_reset_status.posD_change;
}
if (vert_vel_reset) {
_output_new.vel(2) += _state_reset_status.velD_change;
}
// add the reset amount to the output observer buffered data
outputSample output_states;
unsigned output_length = _output_buffer.get_length();
for (unsigned i=0; i < output_length; i++) {
output_states = _output_buffer.get_from_index(i);
if (vert_pos_reset) {
output_states.pos(2) += _state_reset_status.posD_change;
}
if (vert_vel_reset) {
output_states.vel(2) += _state_reset_status.velD_change;
}
_output_buffer.push_to_index(i,output_states);
}
}
void Ekf::fuseAirspeed()
{
// Initialize variables
float vn; // Velocity in north direction
float ve; // Velocity in east direction
float vd; // Velocity in downwards direction
float vwn; // Wind speed in north direction
float vwe; // Wind speed in east direction
float v_tas_pred; // Predicted measurement
float R_TAS = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) * math::constrain(_airspeed_sample_delayed.eas2tas, 0.9f,
10.0f)); // Variance for true airspeed measurement - (m/sec)^2
float SH_TAS[3] = {}; // Varialbe used to optimise calculations of measurement jacobian
float H_TAS[24] = {}; // Observation Jacobian
float SK_TAS[2] = {}; // Varialbe used to optimise calculations of the Kalman gain vector
float Kfusion[24] = {}; // Kalman gain vector
// Copy required states to local variable names
vn = _state.vel(0);
ve = _state.vel(1);
vd = _state.vel(2);
vwn = _state.wind_vel(0);
vwe = _state.wind_vel(1);
// Calculate the predicted airspeed
v_tas_pred = sqrtf((ve - vwe) * (ve - vwe) + (vn - vwn) * (vn - vwn) + vd * vd);
// Perform fusion of True Airspeed measurement
if (v_tas_pred > 1.0f) {
// Calculate the observation jacobian
// intermediate variable from algebraic optimisation
SH_TAS[0] = 1.0f/v_tas_pred;
SH_TAS[1] = (SH_TAS[0]*(2.0f*ve - 2.0f*vwe))*0.5f;
SH_TAS[2] = (SH_TAS[0]*(2.0f*vn - 2.0f*vwn))*0.5f;
for (uint8_t i = 0; i < _k_num_states; i++) {
H_TAS[i] = 0.0f;
}
H_TAS[4] = SH_TAS[2];
H_TAS[5] = SH_TAS[1];
H_TAS[6] = vd*SH_TAS[0];
H_TAS[22] = -SH_TAS[2];
H_TAS[23] = -SH_TAS[1];
// We don't want to update the innovation variance if the calculation is ill conditioned
float _airspeed_innov_var_temp = (R_TAS + SH_TAS[2]*(P[4][4]*SH_TAS[2] + P[5][4]*SH_TAS[1] - P[22][4]*SH_TAS[2] - P[23][4]*SH_TAS[1] + P[6][4]*vd*SH_TAS[0]) + SH_TAS[1]*(P[4][5]*SH_TAS[2] + P[5][5]*SH_TAS[1] - P[22][5]*SH_TAS[2] - P[23][5]*SH_TAS[1] + P[6][5]*vd*SH_TAS[0]) - SH_TAS[2]*(P[4][22]*SH_TAS[2] + P[5][22]*SH_TAS[1] - P[22][22]*SH_TAS[2] - P[23][22]*SH_TAS[1] + P[6][22]*vd*SH_TAS[0]) - SH_TAS[1]*(P[4][23]*SH_TAS[2] + P[5][23]*SH_TAS[1] - P[22][23]*SH_TAS[2] - P[23][23]*SH_TAS[1] + P[6][23]*vd*SH_TAS[0]) + vd*SH_TAS[0]*(P[4][6]*SH_TAS[2] + P[5][6]*SH_TAS[1] - P[22][6]*SH_TAS[2] - P[23][6]*SH_TAS[1] + P[6][6]*vd*SH_TAS[0]));
if (_airspeed_innov_var_temp >= R_TAS) { // Check for badly conditioned calculation
SK_TAS[0] = 1.0f / _airspeed_innov_var_temp;
_fault_status.flags.bad_airspeed = false;
} else { // Reset the estimator covarinace matrix
_fault_status.flags.bad_airspeed = true;
initialiseCovariance();
ECL_ERR("EKF airspeed fusion numerical error - covariance reset");
return;
}
SK_TAS[1] = SH_TAS[1];
Kfusion[0] = SK_TAS[0]*(P[0][4]*SH_TAS[2] - P[0][22]*SH_TAS[2] + P[0][5]*SK_TAS[1] - P[0][23]*SK_TAS[1] + P[0][6]*vd*SH_TAS[0]);
Kfusion[1] = SK_TAS[0]*(P[1][4]*SH_TAS[2] - P[1][22]*SH_TAS[2] + P[1][5]*SK_TAS[1] - P[1][23]*SK_TAS[1] + P[1][6]*vd*SH_TAS[0]);
Kfusion[2] = SK_TAS[0]*(P[2][4]*SH_TAS[2] - P[2][22]*SH_TAS[2] + P[2][5]*SK_TAS[1] - P[2][23]*SK_TAS[1] + P[2][6]*vd*SH_TAS[0]);
Kfusion[3] = SK_TAS[0]*(P[3][4]*SH_TAS[2] - P[3][22]*SH_TAS[2] + P[3][5]*SK_TAS[1] - P[3][23]*SK_TAS[1] + P[3][6]*vd*SH_TAS[0]);
Kfusion[4] = SK_TAS[0]*(P[4][4]*SH_TAS[2] - P[4][22]*SH_TAS[2] + P[4][5]*SK_TAS[1] - P[4][23]*SK_TAS[1] + P[4][6]*vd*SH_TAS[0]);
Kfusion[5] = SK_TAS[0]*(P[5][4]*SH_TAS[2] - P[5][22]*SH_TAS[2] + P[5][5]*SK_TAS[1] - P[5][23]*SK_TAS[1] + P[5][6]*vd*SH_TAS[0]);
Kfusion[6] = SK_TAS[0]*(P[6][4]*SH_TAS[2] - P[6][22]*SH_TAS[2] + P[6][5]*SK_TAS[1] - P[6][23]*SK_TAS[1] + P[6][6]*vd*SH_TAS[0]);
Kfusion[7] = SK_TAS[0]*(P[7][4]*SH_TAS[2] - P[7][22]*SH_TAS[2] + P[7][5]*SK_TAS[1] - P[7][23]*SK_TAS[1] + P[7][6]*vd*SH_TAS[0]);
Kfusion[8] = SK_TAS[0]*(P[8][4]*SH_TAS[2] - P[8][22]*SH_TAS[2] + P[8][5]*SK_TAS[1] - P[8][23]*SK_TAS[1] + P[8][6]*vd*SH_TAS[0]);
Kfusion[9] = SK_TAS[0]*(P[9][4]*SH_TAS[2] - P[9][22]*SH_TAS[2] + P[9][5]*SK_TAS[1] - P[9][23]*SK_TAS[1] + P[9][6]*vd*SH_TAS[0]);
Kfusion[10] = SK_TAS[0]*(P[10][4]*SH_TAS[2] - P[10][22]*SH_TAS[2] + P[10][5]*SK_TAS[1] - P[10][23]*SK_TAS[1] + P[10][6]*vd*SH_TAS[0]);
Kfusion[11] = SK_TAS[0]*(P[11][4]*SH_TAS[2] - P[11][22]*SH_TAS[2] + P[11][5]*SK_TAS[1] - P[11][23]*SK_TAS[1] + P[11][6]*vd*SH_TAS[0]);
Kfusion[12] = SK_TAS[0]*(P[12][4]*SH_TAS[2] - P[12][22]*SH_TAS[2] + P[12][5]*SK_TAS[1] - P[12][23]*SK_TAS[1] + P[12][6]*vd*SH_TAS[0]);
Kfusion[13] = SK_TAS[0]*(P[13][4]*SH_TAS[2] - P[13][22]*SH_TAS[2] + P[13][5]*SK_TAS[1] - P[13][23]*SK_TAS[1] + P[13][6]*vd*SH_TAS[0]);
Kfusion[14] = SK_TAS[0]*(P[14][4]*SH_TAS[2] - P[14][22]*SH_TAS[2] + P[14][5]*SK_TAS[1] - P[14][23]*SK_TAS[1] + P[14][6]*vd*SH_TAS[0]);
Kfusion[15] = SK_TAS[0]*(P[15][4]*SH_TAS[2] - P[15][22]*SH_TAS[2] + P[15][5]*SK_TAS[1] - P[15][23]*SK_TAS[1] + P[15][6]*vd*SH_TAS[0]);
Kfusion[16] = SK_TAS[0]*(P[16][4]*SH_TAS[2] - P[16][22]*SH_TAS[2] + P[16][5]*SK_TAS[1] - P[16][23]*SK_TAS[1] + P[16][6]*vd*SH_TAS[0]);
Kfusion[17] = SK_TAS[0]*(P[17][4]*SH_TAS[2] - P[17][22]*SH_TAS[2] + P[17][5]*SK_TAS[1] - P[17][23]*SK_TAS[1] + P[17][6]*vd*SH_TAS[0]);
Kfusion[18] = SK_TAS[0]*(P[18][4]*SH_TAS[2] - P[18][22]*SH_TAS[2] + P[18][5]*SK_TAS[1] - P[18][23]*SK_TAS[1] + P[18][6]*vd*SH_TAS[0]);
Kfusion[19] = SK_TAS[0]*(P[19][4]*SH_TAS[2] - P[19][22]*SH_TAS[2] + P[19][5]*SK_TAS[1] - P[19][23]*SK_TAS[1] + P[19][6]*vd*SH_TAS[0]);
Kfusion[20] = SK_TAS[0]*(P[20][4]*SH_TAS[2] - P[20][22]*SH_TAS[2] + P[20][5]*SK_TAS[1] - P[20][23]*SK_TAS[1] + P[20][6]*vd*SH_TAS[0]);
Kfusion[21] = SK_TAS[0]*(P[21][4]*SH_TAS[2] - P[21][22]*SH_TAS[2] + P[21][5]*SK_TAS[1] - P[21][23]*SK_TAS[1] + P[21][6]*vd*SH_TAS[0]);
Kfusion[22] = SK_TAS[0]*(P[22][4]*SH_TAS[2] - P[22][22]*SH_TAS[2] + P[22][5]*SK_TAS[1] - P[22][23]*SK_TAS[1] + P[22][6]*vd*SH_TAS[0]);
Kfusion[23] = SK_TAS[0]*(P[23][4]*SH_TAS[2] - P[23][22]*SH_TAS[2] + P[23][5]*SK_TAS[1] - P[23][23]*SK_TAS[1] + P[23][6]*vd*SH_TAS[0]);
// Calculate measurement innovation
_airspeed_innov = v_tas_pred -
_airspeed_sample_delayed.true_airspeed;
// Calculate the innovation variance
_airspeed_innov_var = 1.0f / SK_TAS[0];
// Compute the ratio of innovation to gate size
_tas_test_ratio = sq(_airspeed_innov) / (sq(fmaxf(_params.tas_innov_gate, 1.0f)) * _airspeed_innov_var);
// If the innovation consistency check fails then don't fuse the sample and indicate bad airspeed health
if (_tas_test_ratio > 1.0f) {
_innov_check_fail_status.flags.reject_airspeed = true;
return;
}
else {
_innov_check_fail_status.flags.reject_airspeed = false;
}
// Airspeed measurement sample has passed check so record it
_time_last_arsp_fuse = _time_last_imu;
// apply covariance correction via P_new = (I -K*H)*P
// first calculate expression for KHP
// then calculate P - KHP
for (unsigned row = 0; row < _k_num_states; row++) {
KH[row][4] = Kfusion[row] * H_TAS[4];
KH[row][5] = Kfusion[row] * H_TAS[5];
KH[row][6] = Kfusion[row] * H_TAS[6];
KH[row][22] = Kfusion[row] * H_TAS[22];
KH[row][23] = Kfusion[row] * H_TAS[23];
}
for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) {
float tmp = KH[row][4] * P[4][column];
tmp += KH[row][5] * P[5][column];
tmp += KH[row][6] * P[6][column];
tmp += KH[row][22] * P[22][column];
tmp += KH[row][23] * P[23][column];
KHP[row][column] = tmp;
}
}
// if the covariance correction will result in a negative variance, then
// the covariance marix is unhealthy and must be corrected
bool healthy = true;
_fault_status.flags.bad_airspeed = false;
for (int i = 0; i < _k_num_states; i++) {
if (P[i][i] < KHP[i][i]) {
// zero rows and columns
zeroRows(P,i,i);
zeroCols(P,i,i);
//flag as unhealthy
healthy = false;
// update individual measurement health status
_fault_status.flags.bad_airspeed = true;
}
}
// only apply covariance and state corrrections if healthy
if (healthy) {
// apply the covariance corrections
for (unsigned row = 0; row < _k_num_states; row++) {
for (unsigned column = 0; column < _k_num_states; column++) {
P[row][column] = P[row][column] - KHP[row][column];
}
}
// correct the covariance marix for gross errors
fixCovarianceErrors();
// apply the state corrections
fuse(Kfusion, _airspeed_innov);
}
}
}
