ardupilot/libraries/AP_NavEKF3/AP_NavEKF3_MagFusion.cpp

#include <AP_HAL/AP_HAL.h>

#include "AP_NavEKF3.h"
#include "AP_NavEKF3_core.h"
#include <GCS_MAVLink/GCS.h>

extern const AP_HAL::HAL& hal;

/********************************************************
*                   RESET FUNCTIONS                     *
********************************************************/

// Control reset of yaw and magnetic field states
void NavEKF3_core::controlMagYawReset()
{

    // Vehicles that can use a zero sideslip assumption (Planes) are a special case
    // They can use the GPS velocity to recover from bad initial compass data
    // This allows recovery for heading alignment errors due to compass faults
    if (assume_zero_sideslip() && !finalInflightYawInit && inFlight) {
        gpsYawResetRequest = true;
        return;
    } else {
        gpsYawResetRequest = false;
    }

    // Quaternion and delta rotation vector that are re-used for different calculations
    Vector3f deltaRotVecTemp;
    Quaternion deltaQuatTemp;

    bool flightResetAllowed = false;
    bool initialResetAllowed = false;
    if (!finalInflightYawInit) {
        // Use a quaternion division to calculate the delta quaternion between the rotation at the current and last time
        deltaQuatTemp = stateStruct.quat / prevQuatMagReset;
        prevQuatMagReset = stateStruct.quat;

        // convert the quaternion to a rotation vector and find its length
        deltaQuatTemp.to_axis_angle(deltaRotVecTemp);

        // check if the spin rate is OK - high spin rates can cause angular alignment errors
        bool angRateOK = deltaRotVecTemp.length() < 0.1745f;

        initialResetAllowed = angRateOK;
        flightResetAllowed = angRateOK && !onGround;

    }

    // reset the limit on the number of magnetic anomaly resets for each takeoff
    if (onGround) {
        magYawAnomallyCount = 0;
    }

    // Check if conditions for a interim or final yaw/mag reset are met
    bool finalResetRequest = false;
    bool interimResetRequest = false;
    if (flightResetAllowed && !assume_zero_sideslip()) {
        // check that we have reached a height where ground magnetic interference effects are insignificant
        // and can perform a final reset of the yaw and field states
        finalResetRequest = (stateStruct.position.z  - posDownAtTakeoff) < -EKF3_MAG_FINAL_RESET_ALT;

        // check for increasing height
        bool hgtIncreasing = (posDownAtLastMagReset-stateStruct.position.z) > 0.5f;
        float yawInnovIncrease = fabsf(innovYaw) - fabsf(yawInnovAtLastMagReset);

        // check for increasing yaw innovations
        bool yawInnovIncreasing = yawInnovIncrease > 0.25f;

        // check that the yaw innovations haven't been caused by a large change in attitude
        deltaQuatTemp = quatAtLastMagReset / stateStruct.quat;
        deltaQuatTemp.to_axis_angle(deltaRotVecTemp);
        bool largeAngleChange = deltaRotVecTemp.length() > yawInnovIncrease;

        // if yaw innovations and height have increased and we haven't rotated much
        // then we are climbing away from a ground based magnetic anomaly and need to reset
        interimResetRequest = !finalInflightYawInit
                                && !finalResetRequest
                                && (magYawAnomallyCount < MAG_ANOMALY_RESET_MAX)
                                && hgtIncreasing
                                && yawInnovIncreasing
                                && !largeAngleChange;
    }

    // an initial reset is required if we have not yet aligned the yaw angle
    bool initialResetRequest = initialResetAllowed && !yawAlignComplete;

    // a combined yaw angle and magnetic field reset can be initiated by:
    magYawResetRequest = magYawResetRequest || // an external request
            initialResetRequest || // an initial alignment performed by all vehicle types using magnetometer
            interimResetRequest || // an interim alignment required to recover from ground based magnetic anomaly
            finalResetRequest; // the final reset when we have achieved enough height to be in stable magnetic field environment

    // Perform a reset of magnetic field states and reset yaw to corrected magnetic heading
    if (magYawResetRequest && use_compass()) {
        // set yaw from a single mag sample
        setYawFromMag();

        // send initial alignment status to console
        if (!yawAlignComplete) {
            gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u initial yaw alignment complete",(unsigned)imu_index);
        }

        // send in-flight yaw alignment status to console
        if (finalResetRequest) {
            gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u in-flight yaw alignment complete",(unsigned)imu_index);
        } else if (interimResetRequest) {
            magYawAnomallyCount++;
            gcs().send_text(MAV_SEVERITY_WARNING, "EKF3 IMU%u ground mag anomaly, yaw re-aligned",(unsigned)imu_index);
        }

        // clear the complete flags if an interim reset has been performed to allow subsequent
        // and final reset to occur
        if (interimResetRequest) {
            finalInflightYawInit = false;
            finalInflightMagInit = false;
        }

        // mag states
        if (!magFieldLearned) {
            resetMagFieldStates();
        }
    }

    if (magStateResetRequest) {
        resetMagFieldStates();
    }
}

// this function is used to do a forced re-alignment of the yaw angle to align with the horizontal velocity
// vector from GPS. It is used to align the yaw angle after launch or takeoff.
void NavEKF3_core::realignYawGPS()
{
    // get quaternion from existing filter states and calculate roll, pitch and yaw angles
    Vector3f eulerAngles;
    stateStruct.quat.to_euler(eulerAngles.x, eulerAngles.y, eulerAngles.z);

    if ((sq(gpsDataDelayed.vel.x) + sq(gpsDataDelayed.vel.y)) > 25.0f) {
        // calculate course yaw angle
        float velYaw = atan2f(stateStruct.velocity.y,stateStruct.velocity.x);

        // calculate course yaw angle from GPS velocity
        float gpsYaw = atan2f(gpsDataDelayed.vel.y,gpsDataDelayed.vel.x);

        // Check the yaw angles for consistency
        float yawErr = MAX(fabsf(wrap_PI(gpsYaw - velYaw)),fabsf(wrap_PI(gpsYaw - eulerAngles.z)));

        // If the angles disagree by more than 45 degrees and GPS innovations are large or no previous yaw alignment, we declare the magnetic yaw as bad
        badMagYaw = ((yawErr > 0.7854f) && (velTestRatio > 1.0f) && (PV_AidingMode == AID_ABSOLUTE)) || !yawAlignComplete;

        // correct yaw angle using GPS ground course if compass yaw bad
        if (badMagYaw) {
            // attempt to use EKF-GSF estimate if available as it is more robust to GPS glitches
            if (EKFGSF_resetMainFilterYaw()) {
                return;
            }

            // keep roll and pitch and reset yaw
            resetQuatStateYawOnly(gpsYaw, sq(radians(45.0f)));

            // reset the velocity and position states as they will be inaccurate due to bad yaw
            ResetVelocity(resetDataSource::GPS);
            ResetPosition(resetDataSource::GPS);

            // send yaw alignment information to console
            gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw aligned to GPS velocity",(unsigned)imu_index);

            if (use_compass()) {
                // request a mag field reset which may enable us to use the magnetometer if the previous fault was due to bad initialisation
                magStateResetRequest = true;
                // clear the all sensors failed status so that the magnetometers sensors get a second chance now that we are flying
                allMagSensorsFailed = false;
            }
        }
    }
}

void NavEKF3_core::alignYawAngle()
{
    // calculate the variance for the rotation estimate expressed as a rotation vector
    // this will be used later to reset the quaternion state covariances
    Vector3f angleErrVarVec = calcRotVecVariances();

    if (yawAngDataDelayed.type == 2) {
        Vector3f euler321;
        stateStruct.quat.to_euler(euler321.x, euler321.y, euler321.z);
        stateStruct.quat.from_euler(euler321.x, euler321.y, yawAngDataDelayed.yawAng);
    } else if (yawAngDataDelayed.type == 1) {
        Vector3f euler312 = stateStruct.quat.to_vector312();
        stateStruct.quat.from_vector312(euler312.x, euler312.y, yawAngDataDelayed.yawAng);
    }

    // set the yaw angle variance to a larger value to reflect the uncertainty in yaw
    angleErrVarVec.z = sq(yawAngDataDelayed.yawAngErr);

    // reset the quaternion covariances using the rotation vector variances
    zeroRows(P,0,3);
    zeroCols(P,0,3);
    initialiseQuatCovariances(angleErrVarVec);

    // send yaw alignment information to console
    gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw aligned",(unsigned)imu_index);

    // record the yaw reset event
    recordYawReset();

    // clear any pending yaw reset requests
    gpsYawResetRequest = false;
    magYawResetRequest = false;

}

/********************************************************
*                   FUSE MEASURED_DATA                  *
********************************************************/

// select fusion of magnetometer data
void NavEKF3_core::SelectMagFusion()
{
    // start performance timer
    hal.util->perf_begin(_perf_FuseMagnetometer);

    // clear the flag that lets other processes know that the expensive magnetometer fusion operation has been performed on that time step
    // used for load levelling
    magFusePerformed = false;

    effectiveMagCal = effective_magCal();

    // Handle case where we are not using a yaw sensor of any type and and attempt to reset the yaw in
    // flight using the output from the GSF yaw estimator.
    if (!use_compass() &&
        effectiveMagCal != MagCal::EXTERNAL_YAW &&
        effectiveMagCal != MagCal::EXTERNAL_YAW_FALLBACK) {

        // because this type of reset event is not as time critical, require a continuous history of valid estimates
        if (!yawAlignComplete && EKFGSF_yaw_valid_count >= GSF_YAW_VALID_HISTORY_THRESHOLD) {
            yawAlignComplete = EKFGSF_resetMainFilterYaw();
        }

        if (imuSampleTime_ms - lastSynthYawTime_ms > 140) {
            if (fabsf(prevTnb[0][2]) < fabsf(prevTnb[1][2])) {
                // A 321 rotation order is best conditioned because the X axis is closer to horizontal than the Y axis
                yawAngDataDelayed.type = 2;
            } else {
                // A 312 rotation order is best conditioned because the Y axis is closer to horizontal than the X axis
                yawAngDataDelayed.type = 1;
            }

            float yawEKFGSF, yawVarianceEKFGSF;
            bool canUseEKFGSF = yawEstimator != nullptr &&
                                yawEstimator->getYawData(yawEKFGSF, yawVarianceEKFGSF) &&
                                is_positive(yawVarianceEKFGSF) && yawVarianceEKFGSF < sq(radians(GSF_YAW_ACCURACY_THRESHOLD_DEG));
            if (yawAlignComplete && canUseEKFGSF && !assume_zero_sideslip()) {
                // use the EKF-GSF yaw estimator output as this is more robust than the EKF can achieve without a yaw measurement
                // for non fixed wing platform types
                yawAngDataDelayed.yawAngErr = MAX(sqrtf(yawVarianceEKFGSF), 0.05f);
                yawAngDataDelayed.yawAng = yawEKFGSF;
                fuseEulerYaw(false, true);
            } else {
                // fuse the last dead-reckoned yaw when static to stop yaw drift and estimate yaw gyro bias estimate
                yawAngDataDelayed.yawAngErr = MAX(frontend->_yawNoise, 0.05f);
                if (!onGroundNotMoving) {
                    if (yawAngDataDelayed.type == 2) {
                        yawAngDataDelayed.yawAng = atan2f(prevTnb[0][1], prevTnb[0][0]);
                    } else if (yawAngDataDelayed.type == 1) {
                        yawAngDataDelayed.yawAng = atan2f(-prevTnb[0][1], prevTnb[1][1]);
                    }
                }
                if (onGroundNotMoving) {
                    // fuse last known good yaw angle before we stopped moving to allow yaw bias learning when on ground before flight
                    fuseEulerYaw(false, true);
                } else if (onGround || (sq(P[0][0])+sq(P[1][1])+sq(P[2][2])+sq(P[3][3]) > 0.01f)) {
                    // prevent uncontrolled yaw variance growth by fusing a zero innovation
                    // when not on ground allow more variance growth so yaw can be corrected
                    // by manoeuvring
                    fuseEulerYaw(true, true);
                }
            }
            magTestRatio.zero();
            yawTestRatio = 0.0f;
            lastSynthYawTime_ms = imuSampleTime_ms;
        }
        return;
    }

    // Handle case where we are using an external yaw sensor instead of a magnetomer
    if (effectiveMagCal == MagCal::EXTERNAL_YAW || effectiveMagCal == MagCal::EXTERNAL_YAW_FALLBACK) {
        bool have_fused_gps_yaw = false;
        if (storedYawAng.recall(yawAngDataDelayed,imuDataDelayed.time_ms)) {
            if (tiltAlignComplete && !yawAlignComplete) {
                alignYawAngle();
            } else if (tiltAlignComplete && yawAlignComplete) {
                fuseEulerYaw(false, true);
            }
            have_fused_gps_yaw = true;
            last_gps_yaw_fusion_ms = imuSampleTime_ms;
        } else if (tiltAlignComplete && !yawAlignComplete && (imuSampleTime_ms - lastSynthYawTime_ms > 140)) {
            yawAngDataDelayed.yawAngErr = MAX(frontend->_yawNoise, 0.05f);
            // update the yaw angle using the last estimate which will be used as a static yaw reference when movement stops
            if (fabsf(prevTnb[0][2]) < fabsf(prevTnb[1][2])) {
                // A 321 rotation order is best conditioned because the X axis is closer to horizontal than the Y axis
                if (!onGroundNotMoving) {
                    yawAngDataDelayed.yawAng = atan2f(prevTnb[0][1], prevTnb[0][0]);
                }
                yawAngDataDelayed.type = 2;
            } else {
                // A 312 rotation order is best conditioned because the Y axis is closer to horizontal than the X axis
                if (!onGroundNotMoving) {
                    yawAngDataDelayed.yawAng = atan2f(-prevTnb[0][1], prevTnb[1][1]);
                }
                yawAngDataDelayed.type = 1;
            }
            if (onGroundNotMoving) {
                // fuse last known good yaw angle before we stopped moving to allow yaw bias learning when on ground before flight
                fuseEulerYaw(false, true);
            } else {
                // prevent uncontrolled yaw variance growth by fusing a zero innovation
                fuseEulerYaw(true, true);
            }
            lastSynthYawTime_ms = imuSampleTime_ms;
        }
        if (effectiveMagCal == MagCal::EXTERNAL_YAW) {
            // no fallback
            return;
        }

        // get new mag data into delay buffer
        readMagData();

        if (have_fused_gps_yaw) {
            if (gps_yaw_mag_fallback_active) {
                gps_yaw_mag_fallback_active = false;
                gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw external",(unsigned)imu_index);
            }
            // update mag bias from GPS yaw
            gps_yaw_mag_fallback_ok = learnMagBiasFromGPS();
            return;
        }

        // we don't have GPS yaw data and are configured for
        // fallback. If we've only just lost GPS yaw
        if (imuSampleTime_ms - last_gps_yaw_fusion_ms < 10000) {
            // don't fallback to magnetometer fusion for 10s
            return;
        }
        if (!gps_yaw_mag_fallback_ok) {
            // mag was not consistent enough with GPS to use it as
            // fallback
            return;
        }
        if (!inFlight) {
            // don't fall back if not flying
            return;
        }
        if (!gps_yaw_mag_fallback_active) {
            gps_yaw_mag_fallback_active = true;
            gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw fallback active",(unsigned)imu_index);
        }
        // fall through to magnetometer fusion
    }

    // If we are using the compass and the magnetometer has been unhealthy for too long we declare a timeout
    if (magHealth) {
        magTimeout = false;
        lastHealthyMagTime_ms = imuSampleTime_ms;
    } else if ((imuSampleTime_ms - lastHealthyMagTime_ms) > frontend->magFailTimeLimit_ms && use_compass()) {
        magTimeout = true;
    }

    if (effectiveMagCal != MagCal::EXTERNAL_YAW_FALLBACK) {
        // check for and read new magnetometer measurements. We don't
        // real for EXTERNAL_YAW_FALLBACK as it has already been read
        // above
        readMagData();
    }

    // check for availability of magnetometer or other yaw data to fuse
    magDataToFuse = storedMag.recall(magDataDelayed,imuDataDelayed.time_ms);

    // Control reset of yaw and magnetic field states if we are using compass data
    if (magDataToFuse) {
        controlMagYawReset();
    }

    // determine if conditions are right to start a new fusion cycle
    // wait until the EKF time horizon catches up with the measurement
    bool dataReady = (magDataToFuse && statesInitialised && use_compass() && yawAlignComplete);
    if (dataReady) {
        // use the simple method of declination to maintain heading if we cannot use the magnetic field states
        if(inhibitMagStates || magStateResetRequest || !magStateInitComplete) {
            fuseEulerYaw(false, false);

            // zero the test ratio output from the inactive 3-axis magnetometer fusion
            magTestRatio.zero();

        } else {
            // if we are not doing aiding with earth relative observations (eg GPS) then the declination is
            // maintained by fusing declination as a synthesised observation
            // We also fuse declination if we are using the WMM tables
            if (PV_AidingMode != AID_ABSOLUTE ||
                (frontend->_mag_ef_limit > 0 && have_table_earth_field)) {
                FuseDeclination(0.34f);
            }
            // fuse the three magnetometer componenents using sequential fusion for each axis
            hal.util->perf_begin(_perf_test[0]);
            FuseMagnetometer();
            hal.util->perf_end(_perf_test[0]);
            // zero the test ratio output from the inactive simple magnetometer yaw fusion
            yawTestRatio = 0.0f;
        }
    }

    // If the final yaw reset has been performed and the state variances are sufficiently low
    // record that the earth field has been learned.
    if (!magFieldLearned && finalInflightMagInit) {
        magFieldLearned = (P[16][16] < sq(0.01f)) && (P[17][17] < sq(0.01f)) && (P[18][18] < sq(0.01f));
    }

    // record the last learned field variances
    if (magFieldLearned && !inhibitMagStates) {
        earthMagFieldVar.x = P[16][16];
        earthMagFieldVar.y = P[17][17];
        earthMagFieldVar.z = P[18][18];
        bodyMagFieldVar.x = P[19][19];
        bodyMagFieldVar.y = P[20][20];
        bodyMagFieldVar.z = P[21][21];
    }

    // stop performance timer
    hal.util->perf_end(_perf_FuseMagnetometer);
}

/*
 * Fuse magnetometer 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::FuseMagnetometer()
{
    // declarations
    ftype &q0 = mag_state.q0;
    ftype &q1 = mag_state.q1;
    ftype &q2 = mag_state.q2;
    ftype &q3 = mag_state.q3;
    ftype &magN = mag_state.magN;
    ftype &magE = mag_state.magE;
    ftype &magD = mag_state.magD;
    ftype &magXbias = mag_state.magXbias;
    ftype &magYbias = mag_state.magYbias;
    ftype &magZbias = mag_state.magZbias;
    Matrix3f &DCM = mag_state.DCM;
    Vector3f &MagPred = mag_state.MagPred;
    ftype &R_MAG = mag_state.R_MAG;
    ftype *SH_MAG = &mag_state.SH_MAG[0];
    Vector24 H_MAG;
    Vector5 SK_MX;
    Vector5 SK_MY;
    Vector5 SK_MZ;

    // perform sequential fusion of magnetometer measurements.
    // this assumes that the errors in the different components are
    // uncorrelated which is not true, however in the absence of covariance
    // data fit is the only assumption we can make
    // so we might as well take advantage of the computational efficiencies
    // associated with sequential fusion
    // calculate observation jacobians and Kalman gains

    // copy required states to local variable names
    q0       = stateStruct.quat[0];
    q1       = stateStruct.quat[1];
    q2       = stateStruct.quat[2];
    q3       = stateStruct.quat[3];
    magN     = stateStruct.earth_magfield[0];
    magE     = stateStruct.earth_magfield[1];
    magD     = stateStruct.earth_magfield[2];
    magXbias = stateStruct.body_magfield[0];
    magYbias = stateStruct.body_magfield[1];
    magZbias = stateStruct.body_magfield[2];

    // rotate predicted earth components into body axes and calculate
    // predicted measurements
    DCM[0][0] = q0*q0 + q1*q1 - q2*q2 - q3*q3;
    DCM[0][1] = 2.0f*(q1*q2 + q0*q3);
    DCM[0][2] = 2.0f*(q1*q3-q0*q2);
    DCM[1][0] = 2.0f*(q1*q2 - q0*q3);
    DCM[1][1] = q0*q0 - q1*q1 + q2*q2 - q3*q3;
    DCM[1][2] = 2.0f*(q2*q3 + q0*q1);
    DCM[2][0] = 2.0f*(q1*q3 + q0*q2);
    DCM[2][1] = 2.0f*(q2*q3 - q0*q1);
    DCM[2][2] = q0*q0 - q1*q1 - q2*q2 + q3*q3;
    MagPred[0] = DCM[0][0]*magN + DCM[0][1]*magE  + DCM[0][2]*magD + magXbias;
    MagPred[1] = DCM[1][0]*magN + DCM[1][1]*magE  + DCM[1][2]*magD + magYbias;
    MagPred[2] = DCM[2][0]*magN + DCM[2][1]*magE  + DCM[2][2]*magD + magZbias;

    // calculate the measurement innovation for each axis
    for (uint8_t i = 0; i<=2; i++) {
        innovMag[i] = MagPred[i] - magDataDelayed.mag[i];
    }

    // scale magnetometer observation error with total angular rate to allow for timing errors
    R_MAG = sq(constrain_float(frontend->_magNoise, 0.01f, 0.5f)) + sq(frontend->magVarRateScale*imuDataDelayed.delAng.length() / imuDataDelayed.delAngDT);

    // calculate common expressions used to calculate observation jacobians an innovation variance for each component
    SH_MAG[0] = 2.0f*magD*q3 + 2.0f*magE*q2 + 2.0f*magN*q1;
    SH_MAG[1] = 2.0f*magD*q0 - 2.0f*magE*q1 + 2.0f*magN*q2;
    SH_MAG[2] = 2.0f*magD*q1 + 2.0f*magE*q0 - 2.0f*magN*q3;
    SH_MAG[3] = sq(q3);
    SH_MAG[4] = sq(q2);
    SH_MAG[5] = sq(q1);
    SH_MAG[6] = sq(q0);
    SH_MAG[7] = 2.0f*magN*q0;
    SH_MAG[8] = 2.0f*magE*q3;

    // Calculate the innovation variance for each axis
    // X axis
    varInnovMag[0] = (P[19][19] + R_MAG + P[1][19]*SH_MAG[0] - P[2][19]*SH_MAG[1] + P[3][19]*SH_MAG[2] - P[16][19]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + (2.0f*q0*q3 + 2.0f*q1*q2)*(P[19][17] + P[1][17]*SH_MAG[0] - P[2][17]*SH_MAG[1] + P[3][17]*SH_MAG[2] - P[16][17]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][17]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][17]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][17]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (2.0f*q0*q2 - 2.0f*q1*q3)*(P[19][18] + P[1][18]*SH_MAG[0] - P[2][18]*SH_MAG[1] + P[3][18]*SH_MAG[2] - P[16][18]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][18]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][18]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][18]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P[19][0] + P[1][0]*SH_MAG[0] - P[2][0]*SH_MAG[1] + P[3][0]*SH_MAG[2] - P[16][0]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][0]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][0]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][0]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[17][19]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][19]*(2.0f*q0*q2 - 2.0f*q1*q3) + SH_MAG[0]*(P[19][1] + P[1][1]*SH_MAG[0] - P[2][1]*SH_MAG[1] + P[3][1]*SH_MAG[2] - P[16][1]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][1]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][1]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][1]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - SH_MAG[1]*(P[19][2] + P[1][2]*SH_MAG[0] - P[2][2]*SH_MAG[1] + P[3][2]*SH_MAG[2] - P[16][2]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][2]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][2]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][2]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[2]*(P[19][3] + P[1][3]*SH_MAG[0] - P[2][3]*SH_MAG[1] + P[3][3]*SH_MAG[2] - P[16][3]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][3]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][3]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][3]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6])*(P[19][16] + P[1][16]*SH_MAG[0] - P[2][16]*SH_MAG[1] + P[3][16]*SH_MAG[2] - P[16][16]*(SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6]) + P[17][16]*(2.0f*q0*q3 + 2.0f*q1*q2) - P[18][16]*(2.0f*q0*q2 - 2.0f*q1*q3) + P[0][16]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[0][19]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2));
    if (varInnovMag[0] >= R_MAG) {
        faultStatus.bad_xmag = false;
    } else {
        // the calculation is badly conditioned, so we cannot perform fusion on this step
        // we reset the covariance matrix and try again next measurement
        CovarianceInit();
        faultStatus.bad_xmag = true;
        return;
    }

    // Y axis
    varInnovMag[1] = (P[20][20] + R_MAG + P[0][20]*SH_MAG[2] + P[1][20]*SH_MAG[1] + P[2][20]*SH_MAG[0] - P[17][20]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - (2.0f*q0*q3 - 2.0f*q1*q2)*(P[20][16] + P[0][16]*SH_MAG[2] + P[1][16]*SH_MAG[1] + P[2][16]*SH_MAG[0] - P[17][16]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][16]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][16]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][16]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (2.0f*q0*q1 + 2.0f*q2*q3)*(P[20][18] + P[0][18]*SH_MAG[2] + P[1][18]*SH_MAG[1] + P[2][18]*SH_MAG[0] - P[17][18]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][18]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][18]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][18]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P[20][3] + P[0][3]*SH_MAG[2] + P[1][3]*SH_MAG[1] + P[2][3]*SH_MAG[0] - P[17][3]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][3]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][3]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][3]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - P[16][20]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][20]*(2.0f*q0*q1 + 2.0f*q2*q3) + SH_MAG[2]*(P[20][0] + P[0][0]*SH_MAG[2] + P[1][0]*SH_MAG[1] + P[2][0]*SH_MAG[0] - P[17][0]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][0]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][0]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][0]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[1]*(P[20][1] + P[0][1]*SH_MAG[2] + P[1][1]*SH_MAG[1] + P[2][1]*SH_MAG[0] - P[17][1]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][1]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][1]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][1]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[0]*(P[20][2] + P[0][2]*SH_MAG[2] + P[1][2]*SH_MAG[1] + P[2][2]*SH_MAG[0] - P[17][2]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][2]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][2]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][2]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6])*(P[20][17] + P[0][17]*SH_MAG[2] + P[1][17]*SH_MAG[1] + P[2][17]*SH_MAG[0] - P[17][17]*(SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6]) - P[16][17]*(2.0f*q0*q3 - 2.0f*q1*q2) + P[18][17]*(2.0f*q0*q1 + 2.0f*q2*q3) - P[3][17]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - P[3][20]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2));
    if (varInnovMag[1] >= R_MAG) {
        faultStatus.bad_ymag = false;
    } else {
        // the calculation is badly conditioned, so we cannot perform fusion on this step
        // we reset the covariance matrix and try again next measurement
        CovarianceInit();
        faultStatus.bad_ymag = true;
        return;
    }

    // Z axis
    varInnovMag[2] = (P[21][21] + R_MAG + P[0][21]*SH_MAG[1] - P[1][21]*SH_MAG[2] + P[3][21]*SH_MAG[0] + P[18][21]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + (2.0f*q0*q2 + 2.0f*q1*q3)*(P[21][16] + P[0][16]*SH_MAG[1] - P[1][16]*SH_MAG[2] + P[3][16]*SH_MAG[0] + P[18][16]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][16]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][16]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][16]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - (2.0f*q0*q1 - 2.0f*q2*q3)*(P[21][17] + P[0][17]*SH_MAG[1] - P[1][17]*SH_MAG[2] + P[3][17]*SH_MAG[0] + P[18][17]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][17]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][17]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][17]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)*(P[21][2] + P[0][2]*SH_MAG[1] - P[1][2]*SH_MAG[2] + P[3][2]*SH_MAG[0] + P[18][2]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][2]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][2]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][2]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[16][21]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][21]*(2.0f*q0*q1 - 2.0f*q2*q3) + SH_MAG[1]*(P[21][0] + P[0][0]*SH_MAG[1] - P[1][0]*SH_MAG[2] + P[3][0]*SH_MAG[0] + P[18][0]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][0]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][0]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][0]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) - SH_MAG[2]*(P[21][1] + P[0][1]*SH_MAG[1] - P[1][1]*SH_MAG[2] + P[3][1]*SH_MAG[0] + P[18][1]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][1]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][1]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][1]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + SH_MAG[0]*(P[21][3] + P[0][3]*SH_MAG[1] - P[1][3]*SH_MAG[2] + P[3][3]*SH_MAG[0] + P[18][3]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][3]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][3]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][3]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + (SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6])*(P[21][18] + P[0][18]*SH_MAG[1] - P[1][18]*SH_MAG[2] + P[3][18]*SH_MAG[0] + P[18][18]*(SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6]) + P[16][18]*(2.0f*q0*q2 + 2.0f*q1*q3) - P[17][18]*(2.0f*q0*q1 - 2.0f*q2*q3) + P[2][18]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2)) + P[2][21]*(SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2));
    if (varInnovMag[2] >= R_MAG) {
        faultStatus.bad_zmag = false;
    } else {
        // the calculation is badly conditioned, so we cannot perform fusion on this step
        // we reset the covariance matrix and try again next measurement
        CovarianceInit();
        faultStatus.bad_zmag = true;
        return;
    }

    // calculate the innovation test ratios
    for (uint8_t i = 0; i<=2; i++) {
        magTestRatio[i] = sq(innovMag[i]) / (sq(MAX(0.01f * (float)frontend->_magInnovGate, 1.0f)) * varInnovMag[i]);
    }

    // check the last values from all components and set magnetometer health accordingly
    magHealth = (magTestRatio[0] < 1.0f && magTestRatio[1] < 1.0f && magTestRatio[2] < 1.0f);

    // if the magnetometer is unhealthy, do not proceed further
    if (!magHealth) {
        return;
    }

    for (uint8_t obsIndex = 0; obsIndex <= 2; obsIndex++) {

        if (obsIndex == 0) {

            for (uint8_t i = 0; i<=stateIndexLim; i++) H_MAG[i] = 0.0f;
            H_MAG[0] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
            H_MAG[1] = SH_MAG[0];
            H_MAG[2] = -SH_MAG[1];
            H_MAG[3] = SH_MAG[2];
            H_MAG[16] = SH_MAG[5] - SH_MAG[4] - SH_MAG[3] + SH_MAG[6];
            H_MAG[17] = 2.0f*q0*q3 + 2.0f*q1*q2;
            H_MAG[18] = 2.0f*q1*q3 - 2.0f*q0*q2;
            H_MAG[19] = 1.0f;
            H_MAG[20] = 0.0f;
            H_MAG[21] = 0.0f;

            // calculate Kalman gain
            SK_MX[0] = 1.0f / varInnovMag[0];
            SK_MX[1] = SH_MAG[3] + SH_MAG[4] - SH_MAG[5] - SH_MAG[6];
            SK_MX[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
            SK_MX[3] = 2.0f*q0*q2 - 2.0f*q1*q3;
            SK_MX[4] = 2.0f*q0*q3 + 2.0f*q1*q2;

            Kfusion[0] = SK_MX[0]*(P[0][19] + P[0][1]*SH_MAG[0] - P[0][2]*SH_MAG[1] + P[0][3]*SH_MAG[2] + P[0][0]*SK_MX[2] - P[0][16]*SK_MX[1] + P[0][17]*SK_MX[4] - P[0][18]*SK_MX[3]);
            Kfusion[1] = SK_MX[0]*(P[1][19] + P[1][1]*SH_MAG[0] - P[1][2]*SH_MAG[1] + P[1][3]*SH_MAG[2] + P[1][0]*SK_MX[2] - P[1][16]*SK_MX[1] + P[1][17]*SK_MX[4] - P[1][18]*SK_MX[3]);
            Kfusion[2] = SK_MX[0]*(P[2][19] + P[2][1]*SH_MAG[0] - P[2][2]*SH_MAG[1] + P[2][3]*SH_MAG[2] + P[2][0]*SK_MX[2] - P[2][16]*SK_MX[1] + P[2][17]*SK_MX[4] - P[2][18]*SK_MX[3]);
            Kfusion[3] = SK_MX[0]*(P[3][19] + P[3][1]*SH_MAG[0] - P[3][2]*SH_MAG[1] + P[3][3]*SH_MAG[2] + P[3][0]*SK_MX[2] - P[3][16]*SK_MX[1] + P[3][17]*SK_MX[4] - P[3][18]*SK_MX[3]);
            Kfusion[4] = SK_MX[0]*(P[4][19] + P[4][1]*SH_MAG[0] - P[4][2]*SH_MAG[1] + P[4][3]*SH_MAG[2] + P[4][0]*SK_MX[2] - P[4][16]*SK_MX[1] + P[4][17]*SK_MX[4] - P[4][18]*SK_MX[3]);
            Kfusion[5] = SK_MX[0]*(P[5][19] + P[5][1]*SH_MAG[0] - P[5][2]*SH_MAG[1] + P[5][3]*SH_MAG[2] + P[5][0]*SK_MX[2] - P[5][16]*SK_MX[1] + P[5][17]*SK_MX[4] - P[5][18]*SK_MX[3]);
            Kfusion[6] = SK_MX[0]*(P[6][19] + P[6][1]*SH_MAG[0] - P[6][2]*SH_MAG[1] + P[6][3]*SH_MAG[2] + P[6][0]*SK_MX[2] - P[6][16]*SK_MX[1] + P[6][17]*SK_MX[4] - P[6][18]*SK_MX[3]);
            Kfusion[7] = SK_MX[0]*(P[7][19] + P[7][1]*SH_MAG[0] - P[7][2]*SH_MAG[1] + P[7][3]*SH_MAG[2] + P[7][0]*SK_MX[2] - P[7][16]*SK_MX[1] + P[7][17]*SK_MX[4] - P[7][18]*SK_MX[3]);
            Kfusion[8] = SK_MX[0]*(P[8][19] + P[8][1]*SH_MAG[0] - P[8][2]*SH_MAG[1] + P[8][3]*SH_MAG[2] + P[8][0]*SK_MX[2] - P[8][16]*SK_MX[1] + P[8][17]*SK_MX[4] - P[8][18]*SK_MX[3]);
            Kfusion[9] = SK_MX[0]*(P[9][19] + P[9][1]*SH_MAG[0] - P[9][2]*SH_MAG[1] + P[9][3]*SH_MAG[2] + P[9][0]*SK_MX[2] - P[9][16]*SK_MX[1] + P[9][17]*SK_MX[4] - P[9][18]*SK_MX[3]);

            if (!inhibitDelAngBiasStates) {
                Kfusion[10] = SK_MX[0]*(P[10][19] + P[10][1]*SH_MAG[0] - P[10][2]*SH_MAG[1] + P[10][3]*SH_MAG[2] + P[10][0]*SK_MX[2] - P[10][16]*SK_MX[1] + P[10][17]*SK_MX[4] - P[10][18]*SK_MX[3]);
                Kfusion[11] = SK_MX[0]*(P[11][19] + P[11][1]*SH_MAG[0] - P[11][2]*SH_MAG[1] + P[11][3]*SH_MAG[2] + P[11][0]*SK_MX[2] - P[11][16]*SK_MX[1] + P[11][17]*SK_MX[4] - P[11][18]*SK_MX[3]);
                Kfusion[12] = SK_MX[0]*(P[12][19] + P[12][1]*SH_MAG[0] - P[12][2]*SH_MAG[1] + P[12][3]*SH_MAG[2] + P[12][0]*SK_MX[2] - P[12][16]*SK_MX[1] + P[12][17]*SK_MX[4] - P[12][18]*SK_MX[3]);
            } else {
                // zero indexes 10 to 12 = 3*4 bytes
                memset(&Kfusion[10], 0, 12);
            }

            if (!inhibitDelVelBiasStates) {
                Kfusion[13] = SK_MX[0]*(P[13][19] + P[13][1]*SH_MAG[0] - P[13][2]*SH_MAG[1] + P[13][3]*SH_MAG[2] + P[13][0]*SK_MX[2] - P[13][16]*SK_MX[1] + P[13][17]*SK_MX[4] - P[13][18]*SK_MX[3]);
                Kfusion[14] = SK_MX[0]*(P[14][19] + P[14][1]*SH_MAG[0] - P[14][2]*SH_MAG[1] + P[14][3]*SH_MAG[2] + P[14][0]*SK_MX[2] - P[14][16]*SK_MX[1] + P[14][17]*SK_MX[4] - P[14][18]*SK_MX[3]);
                Kfusion[15] = SK_MX[0]*(P[15][19] + P[15][1]*SH_MAG[0] - P[15][2]*SH_MAG[1] + P[15][3]*SH_MAG[2] + P[15][0]*SK_MX[2] - P[15][16]*SK_MX[1] + P[15][17]*SK_MX[4] - P[15][18]*SK_MX[3]);
            } else {
                // zero indexes 13 to 15 = 3*4 bytes
                memset(&Kfusion[13], 0, 12);
            }
            // zero Kalman gains to inhibit magnetic field state estimation
            if (!inhibitMagStates) {
                Kfusion[16] = SK_MX[0]*(P[16][19] + P[16][1]*SH_MAG[0] - P[16][2]*SH_MAG[1] + P[16][3]*SH_MAG[2] + P[16][0]*SK_MX[2] - P[16][16]*SK_MX[1] + P[16][17]*SK_MX[4] - P[16][18]*SK_MX[3]);
                Kfusion[17] = SK_MX[0]*(P[17][19] + P[17][1]*SH_MAG[0] - P[17][2]*SH_MAG[1] + P[17][3]*SH_MAG[2] + P[17][0]*SK_MX[2] - P[17][16]*SK_MX[1] + P[17][17]*SK_MX[4] - P[17][18]*SK_MX[3]);
                Kfusion[18] = SK_MX[0]*(P[18][19] + P[18][1]*SH_MAG[0] - P[18][2]*SH_MAG[1] + P[18][3]*SH_MAG[2] + P[18][0]*SK_MX[2] - P[18][16]*SK_MX[1] + P[18][17]*SK_MX[4] - P[18][18]*SK_MX[3]);
                Kfusion[19] = SK_MX[0]*(P[19][19] + P[19][1]*SH_MAG[0] - P[19][2]*SH_MAG[1] + P[19][3]*SH_MAG[2] + P[19][0]*SK_MX[2] - P[19][16]*SK_MX[1] + P[19][17]*SK_MX[4] - P[19][18]*SK_MX[3]);
                Kfusion[20] = SK_MX[0]*(P[20][19] + P[20][1]*SH_MAG[0] - P[20][2]*SH_MAG[1] + P[20][3]*SH_MAG[2] + P[20][0]*SK_MX[2] - P[20][16]*SK_MX[1] + P[20][17]*SK_MX[4] - P[20][18]*SK_MX[3]);
                Kfusion[21] = SK_MX[0]*(P[21][19] + P[21][1]*SH_MAG[0] - P[21][2]*SH_MAG[1] + P[21][3]*SH_MAG[2] + P[21][0]*SK_MX[2] - P[21][16]*SK_MX[1] + P[21][17]*SK_MX[4] - P[21][18]*SK_MX[3]);
            } else {
                // zero indexes 16 to 21 = 6*4 bytes
                memset(&Kfusion[16], 0, 24);
            }

            // zero Kalman gains to inhibit wind state estimation
            if (!inhibitWindStates) {
                Kfusion[22] = SK_MX[0]*(P[22][19] + P[22][1]*SH_MAG[0] - P[22][2]*SH_MAG[1] + P[22][3]*SH_MAG[2] + P[22][0]*SK_MX[2] - P[22][16]*SK_MX[1] + P[22][17]*SK_MX[4] - P[22][18]*SK_MX[3]);
                Kfusion[23] = SK_MX[0]*(P[23][19] + P[23][1]*SH_MAG[0] - P[23][2]*SH_MAG[1] + P[23][3]*SH_MAG[2] + P[23][0]*SK_MX[2] - P[23][16]*SK_MX[1] + P[23][17]*SK_MX[4] - P[23][18]*SK_MX[3]);
            } else {
                // zero indexes 22 to 23 = 2*4 bytes
                memset(&Kfusion[22], 0, 8);
            }

            // set flags to indicate to other processes that fusion has been performed and is required on the next frame
            // this can be used by other fusion processes to avoid fusing on the same frame as this expensive step
            magFusePerformed = true;
            magFuseRequired = true;

        } else if (obsIndex == 1) { // Fuse Y axis

            // calculate observation jacobians
            for (uint8_t i = 0; i<=stateIndexLim; i++) H_MAG[i] = 0.0f;
            H_MAG[0] = SH_MAG[2];
            H_MAG[1] = SH_MAG[1];
            H_MAG[2] = SH_MAG[0];
            H_MAG[3] = 2.0f*magD*q2 - SH_MAG[8] - SH_MAG[7];
            H_MAG[16] = 2.0f*q1*q2 - 2.0f*q0*q3;
            H_MAG[17] = SH_MAG[4] - SH_MAG[3] - SH_MAG[5] + SH_MAG[6];
            H_MAG[18] = 2.0f*q0*q1 + 2.0f*q2*q3;
            H_MAG[19] = 0.0f;
            H_MAG[20] = 1.0f;
            H_MAG[21] = 0.0f;

            // calculate Kalman gain
            SK_MY[0] = 1.0f / varInnovMag[1];
            SK_MY[1] = SH_MAG[3] - SH_MAG[4] + SH_MAG[5] - SH_MAG[6];
            SK_MY[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
            SK_MY[3] = 2.0f*q0*q3 - 2.0f*q1*q2;
            SK_MY[4] = 2.0f*q0*q1 + 2.0f*q2*q3;

            Kfusion[0] = SK_MY[0]*(P[0][20] + P[0][0]*SH_MAG[2] + P[0][1]*SH_MAG[1] + P[0][2]*SH_MAG[0] - P[0][3]*SK_MY[2] - P[0][17]*SK_MY[1] - P[0][16]*SK_MY[3] + P[0][18]*SK_MY[4]);
            Kfusion[1] = SK_MY[0]*(P[1][20] + P[1][0]*SH_MAG[2] + P[1][1]*SH_MAG[1] + P[1][2]*SH_MAG[0] - P[1][3]*SK_MY[2] - P[1][17]*SK_MY[1] - P[1][16]*SK_MY[3] + P[1][18]*SK_MY[4]);
            Kfusion[2] = SK_MY[0]*(P[2][20] + P[2][0]*SH_MAG[2] + P[2][1]*SH_MAG[1] + P[2][2]*SH_MAG[0] - P[2][3]*SK_MY[2] - P[2][17]*SK_MY[1] - P[2][16]*SK_MY[3] + P[2][18]*SK_MY[4]);
            Kfusion[3] = SK_MY[0]*(P[3][20] + P[3][0]*SH_MAG[2] + P[3][1]*SH_MAG[1] + P[3][2]*SH_MAG[0] - P[3][3]*SK_MY[2] - P[3][17]*SK_MY[1] - P[3][16]*SK_MY[3] + P[3][18]*SK_MY[4]);
            Kfusion[4] = SK_MY[0]*(P[4][20] + P[4][0]*SH_MAG[2] + P[4][1]*SH_MAG[1] + P[4][2]*SH_MAG[0] - P[4][3]*SK_MY[2] - P[4][17]*SK_MY[1] - P[4][16]*SK_MY[3] + P[4][18]*SK_MY[4]);
            Kfusion[5] = SK_MY[0]*(P[5][20] + P[5][0]*SH_MAG[2] + P[5][1]*SH_MAG[1] + P[5][2]*SH_MAG[0] - P[5][3]*SK_MY[2] - P[5][17]*SK_MY[1] - P[5][16]*SK_MY[3] + P[5][18]*SK_MY[4]);
            Kfusion[6] = SK_MY[0]*(P[6][20] + P[6][0]*SH_MAG[2] + P[6][1]*SH_MAG[1] + P[6][2]*SH_MAG[0] - P[6][3]*SK_MY[2] - P[6][17]*SK_MY[1] - P[6][16]*SK_MY[3] + P[6][18]*SK_MY[4]);
            Kfusion[7] = SK_MY[0]*(P[7][20] + P[7][0]*SH_MAG[2] + P[7][1]*SH_MAG[1] + P[7][2]*SH_MAG[0] - P[7][3]*SK_MY[2] - P[7][17]*SK_MY[1] - P[7][16]*SK_MY[3] + P[7][18]*SK_MY[4]);
            Kfusion[8] = SK_MY[0]*(P[8][20] + P[8][0]*SH_MAG[2] + P[8][1]*SH_MAG[1] + P[8][2]*SH_MAG[0] - P[8][3]*SK_MY[2] - P[8][17]*SK_MY[1] - P[8][16]*SK_MY[3] + P[8][18]*SK_MY[4]);
            Kfusion[9] = SK_MY[0]*(P[9][20] + P[9][0]*SH_MAG[2] + P[9][1]*SH_MAG[1] + P[9][2]*SH_MAG[0] - P[9][3]*SK_MY[2] - P[9][17]*SK_MY[1] - P[9][16]*SK_MY[3] + P[9][18]*SK_MY[4]);

            if (!inhibitDelAngBiasStates) {
                Kfusion[10] = SK_MY[0]*(P[10][20] + P[10][0]*SH_MAG[2] + P[10][1]*SH_MAG[1] + P[10][2]*SH_MAG[0] - P[10][3]*SK_MY[2] - P[10][17]*SK_MY[1] - P[10][16]*SK_MY[3] + P[10][18]*SK_MY[4]);
                Kfusion[11] = SK_MY[0]*(P[11][20] + P[11][0]*SH_MAG[2] + P[11][1]*SH_MAG[1] + P[11][2]*SH_MAG[0] - P[11][3]*SK_MY[2] - P[11][17]*SK_MY[1] - P[11][16]*SK_MY[3] + P[11][18]*SK_MY[4]);
                Kfusion[12] = SK_MY[0]*(P[12][20] + P[12][0]*SH_MAG[2] + P[12][1]*SH_MAG[1] + P[12][2]*SH_MAG[0] - P[12][3]*SK_MY[2] - P[12][17]*SK_MY[1] - P[12][16]*SK_MY[3] + P[12][18]*SK_MY[4]);
            } else {
                // zero indexes 10 to 12 = 3*4 bytes
                memset(&Kfusion[10], 0, 12);
            }

            if (!inhibitDelVelBiasStates) {
                Kfusion[13] = SK_MY[0]*(P[13][20] + P[13][0]*SH_MAG[2] + P[13][1]*SH_MAG[1] + P[13][2]*SH_MAG[0] - P[13][3]*SK_MY[2] - P[13][17]*SK_MY[1] - P[13][16]*SK_MY[3] + P[13][18]*SK_MY[4]);
                Kfusion[14] = SK_MY[0]*(P[14][20] + P[14][0]*SH_MAG[2] + P[14][1]*SH_MAG[1] + P[14][2]*SH_MAG[0] - P[14][3]*SK_MY[2] - P[14][17]*SK_MY[1] - P[14][16]*SK_MY[3] + P[14][18]*SK_MY[4]);
                Kfusion[15] = SK_MY[0]*(P[15][20] + P[15][0]*SH_MAG[2] + P[15][1]*SH_MAG[1] + P[15][2]*SH_MAG[0] - P[15][3]*SK_MY[2] - P[15][17]*SK_MY[1] - P[15][16]*SK_MY[3] + P[15][18]*SK_MY[4]);
            } else {
                // zero indexes 13 to 15 = 3*4 bytes
                memset(&Kfusion[13], 0, 12);
            }

            // zero Kalman gains to inhibit magnetic field state estimation
            if (!inhibitMagStates) {
                Kfusion[16] = SK_MY[0]*(P[16][20] + P[16][0]*SH_MAG[2] + P[16][1]*SH_MAG[1] + P[16][2]*SH_MAG[0] - P[16][3]*SK_MY[2] - P[16][17]*SK_MY[1] - P[16][16]*SK_MY[3] + P[16][18]*SK_MY[4]);
                Kfusion[17] = SK_MY[0]*(P[17][20] + P[17][0]*SH_MAG[2] + P[17][1]*SH_MAG[1] + P[17][2]*SH_MAG[0] - P[17][3]*SK_MY[2] - P[17][17]*SK_MY[1] - P[17][16]*SK_MY[3] + P[17][18]*SK_MY[4]);
                Kfusion[18] = SK_MY[0]*(P[18][20] + P[18][0]*SH_MAG[2] + P[18][1]*SH_MAG[1] + P[18][2]*SH_MAG[0] - P[18][3]*SK_MY[2] - P[18][17]*SK_MY[1] - P[18][16]*SK_MY[3] + P[18][18]*SK_MY[4]);
                Kfusion[19] = SK_MY[0]*(P[19][20] + P[19][0]*SH_MAG[2] + P[19][1]*SH_MAG[1] + P[19][2]*SH_MAG[0] - P[19][3]*SK_MY[2] - P[19][17]*SK_MY[1] - P[19][16]*SK_MY[3] + P[19][18]*SK_MY[4]);
                Kfusion[20] = SK_MY[0]*(P[20][20] + P[20][0]*SH_MAG[2] + P[20][1]*SH_MAG[1] + P[20][2]*SH_MAG[0] - P[20][3]*SK_MY[2] - P[20][17]*SK_MY[1] - P[20][16]*SK_MY[3] + P[20][18]*SK_MY[4]);
                Kfusion[21] = SK_MY[0]*(P[21][20] + P[21][0]*SH_MAG[2] + P[21][1]*SH_MAG[1] + P[21][2]*SH_MAG[0] - P[21][3]*SK_MY[2] - P[21][17]*SK_MY[1] - P[21][16]*SK_MY[3] + P[21][18]*SK_MY[4]);
            } else {
                // zero indexes 16 to 21 = 6*4 bytes
                memset(&Kfusion[16], 0, 24);
            }

            // zero Kalman gains to inhibit wind state estimation
            if (!inhibitWindStates) {
                Kfusion[22] = SK_MY[0]*(P[22][20] + P[22][0]*SH_MAG[2] + P[22][1]*SH_MAG[1] + P[22][2]*SH_MAG[0] - P[22][3]*SK_MY[2] - P[22][17]*SK_MY[1] - P[22][16]*SK_MY[3] + P[22][18]*SK_MY[4]);
                Kfusion[23] = SK_MY[0]*(P[23][20] + P[23][0]*SH_MAG[2] + P[23][1]*SH_MAG[1] + P[23][2]*SH_MAG[0] - P[23][3]*SK_MY[2] - P[23][17]*SK_MY[1] - P[23][16]*SK_MY[3] + P[23][18]*SK_MY[4]);
            } else {
                // zero indexes 22 to 23 = 2*4 bytes
                memset(&Kfusion[22], 0, 8);
            }

            // set flags to indicate to other processes that fusion has been performed and is required on the next frame
            // this can be used by other fusion processes to avoid fusing on the same frame as this expensive step
            magFusePerformed = true;
            magFuseRequired = true;
        }
        else if (obsIndex == 2) // we are now fusing the Z measurement
        {
            // calculate observation jacobians
            for (uint8_t i = 0; i<=stateIndexLim; i++) H_MAG[i] = 0.0f;
            H_MAG[0] = SH_MAG[1];
            H_MAG[1] = -SH_MAG[2];
            H_MAG[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
            H_MAG[3] = SH_MAG[0];
            H_MAG[16] = 2.0f*q0*q2 + 2.0f*q1*q3;
            H_MAG[17] = 2.0f*q2*q3 - 2.0f*q0*q1;
            H_MAG[18] = SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6];
            H_MAG[19] = 0.0f;
            H_MAG[20] = 0.0f;
            H_MAG[21] = 1.0f;

            // calculate Kalman gain
            SK_MZ[0] = 1.0f / varInnovMag[2];
            SK_MZ[1] = SH_MAG[3] - SH_MAG[4] - SH_MAG[5] + SH_MAG[6];
            SK_MZ[2] = SH_MAG[7] + SH_MAG[8] - 2.0f*magD*q2;
            SK_MZ[3] = 2.0f*q0*q1 - 2.0f*q2*q3;
            SK_MZ[4] = 2.0f*q0*q2 + 2.0f*q1*q3;

            Kfusion[0] = SK_MZ[0]*(P[0][21] + P[0][0]*SH_MAG[1] - P[0][1]*SH_MAG[2] + P[0][3]*SH_MAG[0] + P[0][2]*SK_MZ[2] + P[0][18]*SK_MZ[1] + P[0][16]*SK_MZ[4] - P[0][17]*SK_MZ[3]);
            Kfusion[1] = SK_MZ[0]*(P[1][21] + P[1][0]*SH_MAG[1] - P[1][1]*SH_MAG[2] + P[1][3]*SH_MAG[0] + P[1][2]*SK_MZ[2] + P[1][18]*SK_MZ[1] + P[1][16]*SK_MZ[4] - P[1][17]*SK_MZ[3]);
            Kfusion[2] = SK_MZ[0]*(P[2][21] + P[2][0]*SH_MAG[1] - P[2][1]*SH_MAG[2] + P[2][3]*SH_MAG[0] + P[2][2]*SK_MZ[2] + P[2][18]*SK_MZ[1] + P[2][16]*SK_MZ[4] - P[2][17]*SK_MZ[3]);
            Kfusion[3] = SK_MZ[0]*(P[3][21] + P[3][0]*SH_MAG[1] - P[3][1]*SH_MAG[2] + P[3][3]*SH_MAG[0] + P[3][2]*SK_MZ[2] + P[3][18]*SK_MZ[1] + P[3][16]*SK_MZ[4] - P[3][17]*SK_MZ[3]);
            Kfusion[4] = SK_MZ[0]*(P[4][21] + P[4][0]*SH_MAG[1] - P[4][1]*SH_MAG[2] + P[4][3]*SH_MAG[0] + P[4][2]*SK_MZ[2] + P[4][18]*SK_MZ[1] + P[4][16]*SK_MZ[4] - P[4][17]*SK_MZ[3]);
            Kfusion[5] = SK_MZ[0]*(P[5][21] + P[5][0]*SH_MAG[1] - P[5][1]*SH_MAG[2] + P[5][3]*SH_MAG[0] + P[5][2]*SK_MZ[2] + P[5][18]*SK_MZ[1] + P[5][16]*SK_MZ[4] - P[5][17]*SK_MZ[3]);
            Kfusion[6] = SK_MZ[0]*(P[6][21] + P[6][0]*SH_MAG[1] - P[6][1]*SH_MAG[2] + P[6][3]*SH_MAG[0] + P[6][2]*SK_MZ[2] + P[6][18]*SK_MZ[1] + P[6][16]*SK_MZ[4] - P[6][17]*SK_MZ[3]);
            Kfusion[7] = SK_MZ[0]*(P[7][21] + P[7][0]*SH_MAG[1] - P[7][1]*SH_MAG[2] + P[7][3]*SH_MAG[0] + P[7][2]*SK_MZ[2] + P[7][18]*SK_MZ[1] + P[7][16]*SK_MZ[4] - P[7][17]*SK_MZ[3]);
            Kfusion[8] = SK_MZ[0]*(P[8][21] + P[8][0]*SH_MAG[1] - P[8][1]*SH_MAG[2] + P[8][3]*SH_MAG[0] + P[8][2]*SK_MZ[2] + P[8][18]*SK_MZ[1] + P[8][16]*SK_MZ[4] - P[8][17]*SK_MZ[3]);
            Kfusion[9] = SK_MZ[0]*(P[9][21] + P[9][0]*SH_MAG[1] - P[9][1]*SH_MAG[2] + P[9][3]*SH_MAG[0] + P[9][2]*SK_MZ[2] + P[9][18]*SK_MZ[1] + P[9][16]*SK_MZ[4] - P[9][17]*SK_MZ[3]);

            if (!inhibitDelAngBiasStates) {
                Kfusion[10] = SK_MZ[0]*(P[10][21] + P[10][0]*SH_MAG[1] - P[10][1]*SH_MAG[2] + P[10][3]*SH_MAG[0] + P[10][2]*SK_MZ[2] + P[10][18]*SK_MZ[1] + P[10][16]*SK_MZ[4] - P[10][17]*SK_MZ[3]);
                Kfusion[11] = SK_MZ[0]*(P[11][21] + P[11][0]*SH_MAG[1] - P[11][1]*SH_MAG[2] + P[11][3]*SH_MAG[0] + P[11][2]*SK_MZ[2] + P[11][18]*SK_MZ[1] + P[11][16]*SK_MZ[4] - P[11][17]*SK_MZ[3]);
                Kfusion[12] = SK_MZ[0]*(P[12][21] + P[12][0]*SH_MAG[1] - P[12][1]*SH_MAG[2] + P[12][3]*SH_MAG[0] + P[12][2]*SK_MZ[2] + P[12][18]*SK_MZ[1] + P[12][16]*SK_MZ[4] - P[12][17]*SK_MZ[3]);
            } else {
                // zero indexes 10 to 12 = 3*4 bytes
                memset(&Kfusion[10], 0, 12);
            }

            if (!inhibitDelVelBiasStates) {
                Kfusion[13] = SK_MZ[0]*(P[13][21] + P[13][0]*SH_MAG[1] - P[13][1]*SH_MAG[2] + P[13][3]*SH_MAG[0] + P[13][2]*SK_MZ[2] + P[13][18]*SK_MZ[1] + P[13][16]*SK_MZ[4] - P[13][17]*SK_MZ[3]);
                Kfusion[14] = SK_MZ[0]*(P[14][21] + P[14][0]*SH_MAG[1] - P[14][1]*SH_MAG[2] + P[14][3]*SH_MAG[0] + P[14][2]*SK_MZ[2] + P[14][18]*SK_MZ[1] + P[14][16]*SK_MZ[4] - P[14][17]*SK_MZ[3]);
                Kfusion[15] = SK_MZ[0]*(P[15][21] + P[15][0]*SH_MAG[1] - P[15][1]*SH_MAG[2] + P[15][3]*SH_MAG[0] + P[15][2]*SK_MZ[2] + P[15][18]*SK_MZ[1] + P[15][16]*SK_MZ[4] - P[15][17]*SK_MZ[3]);
            } else {
                // zero indexes 13 to 15 = 3*4 bytes
                memset(&Kfusion[13], 0, 12);
            }

            // zero Kalman gains to inhibit magnetic field state estimation
            if (!inhibitMagStates) {
                Kfusion[16] = SK_MZ[0]*(P[16][21] + P[16][0]*SH_MAG[1] - P[16][1]*SH_MAG[2] + P[16][3]*SH_MAG[0] + P[16][2]*SK_MZ[2] + P[16][18]*SK_MZ[1] + P[16][16]*SK_MZ[4] - P[16][17]*SK_MZ[3]);
                Kfusion[17] = SK_MZ[0]*(P[17][21] + P[17][0]*SH_MAG[1] - P[17][1]*SH_MAG[2] + P[17][3]*SH_MAG[0] + P[17][2]*SK_MZ[2] + P[17][18]*SK_MZ[1] + P[17][16]*SK_MZ[4] - P[17][17]*SK_MZ[3]);
                Kfusion[18] = SK_MZ[0]*(P[18][21] + P[18][0]*SH_MAG[1] - P[18][1]*SH_MAG[2] + P[18][3]*SH_MAG[0] + P[18][2]*SK_MZ[2] + P[18][18]*SK_MZ[1] + P[18][16]*SK_MZ[4] - P[18][17]*SK_MZ[3]);
                Kfusion[19] = SK_MZ[0]*(P[19][21] + P[19][0]*SH_MAG[1] - P[19][1]*SH_MAG[2] + P[19][3]*SH_MAG[0] + P[19][2]*SK_MZ[2] + P[19][18]*SK_MZ[1] + P[19][16]*SK_MZ[4] - P[19][17]*SK_MZ[3]);
                Kfusion[20] = SK_MZ[0]*(P[20][21] + P[20][0]*SH_MAG[1] - P[20][1]*SH_MAG[2] + P[20][3]*SH_MAG[0] + P[20][2]*SK_MZ[2] + P[20][18]*SK_MZ[1] + P[20][16]*SK_MZ[4] - P[20][17]*SK_MZ[3]);
                Kfusion[21] = SK_MZ[0]*(P[21][21] + P[21][0]*SH_MAG[1] - P[21][1]*SH_MAG[2] + P[21][3]*SH_MAG[0] + P[21][2]*SK_MZ[2] + P[21][18]*SK_MZ[1] + P[21][16]*SK_MZ[4] - P[21][17]*SK_MZ[3]);
            } else {
                // zero indexes 16 to 21 = 6*4 bytes
                memset(&Kfusion[16], 0, 24);
            }

            // zero Kalman gains to inhibit wind state estimation
            if (!inhibitWindStates) {
                Kfusion[22] = SK_MZ[0]*(P[22][21] + P[22][0]*SH_MAG[1] - P[22][1]*SH_MAG[2] + P[22][3]*SH_MAG[0] + P[22][2]*SK_MZ[2] + P[22][18]*SK_MZ[1] + P[22][16]*SK_MZ[4] - P[22][17]*SK_MZ[3]);
                Kfusion[23] = SK_MZ[0]*(P[23][21] + P[23][0]*SH_MAG[1] - P[23][1]*SH_MAG[2] + P[23][3]*SH_MAG[0] + P[23][2]*SK_MZ[2] + P[23][18]*SK_MZ[1] + P[23][16]*SK_MZ[4] - P[23][17]*SK_MZ[3]);
            } else {
                // zero indexes 22 to 23 = 2*4 bytes
                memset(&Kfusion[22], 0, 8);
            }

            // set flags to indicate to other processes that fusion has been performed and is required on the next frame
            // this can be used by other fusion processes to avoid fusing on the same frame as this expensive step
            magFusePerformed = true;
        }
        // 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<=3; j++) {
                KH[i][j] = Kfusion[i] * H_MAG[j];
            }
            for (unsigned j = 4; j<=15; j++) {
                KH[i][j] = 0.0f;
            }
            for (unsigned j = 16; j<=21; j++) {
                KH[i][j] = Kfusion[i] * H_MAG[j];
            }
            for (unsigned j = 22; j<=23; j++) {
                KH[i][j] = 0.0f;
            }
        }
        for (unsigned j = 0; j<=stateIndexLim; j++) {
            for (unsigned i = 0; i<=stateIndexLim; i++) {
                ftype res = 0;
                res += KH[i][0] * P[0][j];
                res += KH[i][1] * P[1][j];
                res += KH[i][2] * P[2][j];
                res += KH[i][3] * P[3][j];
                res += KH[i][16] * P[16][j];
                res += KH[i][17] * P[17][j];
                res += KH[i][18] * P[18][j];
                res += KH[i][19] * P[19][j];
                res += KH[i][20] * P[20][j];
                res += KH[i][21] * P[21][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] * innovMag[obsIndex];
            }

            // add table constraint here for faster convergence
            if (have_table_earth_field && frontend->_mag_ef_limit > 0) {
                MagTableConstrain();
            }

            stateStruct.quat.normalize();

        } else {
            // record bad axis
            if (obsIndex == 0) {
                faultStatus.bad_xmag = true;
            } else if (obsIndex == 1) {
                faultStatus.bad_ymag = true;
            } else if (obsIndex == 2) {
                faultStatus.bad_zmag = true;
            }
            CovarianceInit();
            return;
        }
    }
}


/*
 * Fuse magnetic heading measurement 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
 * This fusion method only modifies the orientation, does not require use of the magnetic field states and is computationally cheaper.
 * It is suitable for use when the external magnetic field environment is disturbed (eg close to metal structures, on ground).
 * It is not as robust to magnetometer failures.
 * It is not suitable for operation where the horizontal magnetic field strength is weak (within 30 degrees latitude of the magnetic poles)
 *
 * The following booleans are passed to the function to control the fusion process:
 *
 * usePredictedYaw -  Set this to true if no valid yaw measurement will be available for an extended periods.
 *                    This uses an innovation set to zero which prevents uncontrolled quaternion covariance
 *                    growth or if available, a yaw estimate from the Gaussian Sum Filter.
 * UseExternalYawSensor - Set this to true if yaw data from an external yaw sensor (GPS or external nav) is being used instead of the magnetometer.
*/
void NavEKF3_core::fuseEulerYaw(bool usePredictedYaw, bool useExternalYawSensor)
{
    float q0 = stateStruct.quat[0];
    float q1 = stateStruct.quat[1];
    float q2 = stateStruct.quat[2];
    float q3 = stateStruct.quat[3];

    // external yaw available check
    bool canUseGsfYaw = false;
    float gsfYaw = 0.0f;
    float gsfYawVariance = 0.0f;
    if (usePredictedYaw && yawEstimator != nullptr) {
        canUseGsfYaw = yawEstimator->getYawData(gsfYaw, gsfYawVariance)
                        && is_positive(gsfYawVariance)
                        && gsfYawVariance < sq(radians(GSF_YAW_ACCURACY_THRESHOLD_DEG));
    }

    // yaw measurement error variance (rad^2)
    float R_YAW;
    if (canUseGsfYaw) {
        R_YAW = gsfYawVariance;
    } else if (!useExternalYawSensor) {
        R_YAW = sq(frontend->_yawNoise);
    } else {
        R_YAW = sq(yawAngDataDelayed.yawAngErr);
    }

    // determine if a 321 or 312 Euler sequence is best
    bool useEuler321 = true;
    if (useExternalYawSensor) {
        // If using an external sensor, the definition of yaw is specified through the sensor interface
        if (yawAngDataDelayed.type == 2) {
            useEuler321 = true;
        } else if (yawAngDataDelayed.type == 1) {
            useEuler321 = false;
        } else {
            // invalid selection
            return;
        }
    } else {
        // if using the magnetometer, it is determined automatically
        useEuler321 = (fabsf(prevTnb[0][2]) < fabsf(prevTnb[1][2]));
    }

    // calculate observation jacobian, predicted yaw and zero yaw body to earth rotation matrix
    float yawAngPredicted;
    float H_YAW[4];
    Matrix3f Tbn_zeroYaw;

    if (useEuler321) {
        // calculate observation jacobian when we are observing the first rotation in a 321 sequence
        float t9 = q0*q3;
        float t10 = q1*q2;
        float t2 = t9+t10;
        float t3 = q0*q0;
        float t4 = q1*q1;
        float t5 = q2*q2;
        float t6 = q3*q3;
        float t7 = t3+t4-t5-t6;
        float t8 = t7*t7;
        if (t8 > 1e-6f) {
            t8 = 1.0f/t8;
        } else {
            return;
        }
        float t11 = t2*t2;
        float t12 = t8*t11*4.0f;
        float t13 = t12+1.0f;
        float t14;
        if (fabsf(t13) > 1e-6f) {
            t14 = 1.0f/t13;
        } else {
            return;
        }

        H_YAW[0] = t8*t14*(q3*t3-q3*t4+q3*t5+q3*t6+q0*q1*q2*2.0f)*-2.0f;
        H_YAW[1] = t8*t14*(-q2*t3+q2*t4+q2*t5+q2*t6+q0*q1*q3*2.0f)*-2.0f;
        H_YAW[2] = t8*t14*(q1*t3+q1*t4+q1*t5-q1*t6+q0*q2*q3*2.0f)*2.0f;
        H_YAW[3] = t8*t14*(q0*t3+q0*t4-q0*t5+q0*t6+q1*q2*q3*2.0f)*2.0f;

        // Get the 321 euler angles
        Vector3f euler321;
        stateStruct.quat.to_euler(euler321.x, euler321.y, euler321.z);
        yawAngPredicted = euler321.z;

        // set the yaw to zero and calculate the zero yaw rotation from body to earth frame
        Tbn_zeroYaw.from_euler(euler321.x, euler321.y, 0.0f);

    } else {
        // calculate observation jacobian when we are observing a rotation in a 312 sequence
        float t9 = q0*q3;
        float t10 = q1*q2;
        float t2 = t9-t10;
        float t3 = q0*q0;
        float t4 = q1*q1;
        float t5 = q2*q2;
        float t6 = q3*q3;
        float t7 = t3-t4+t5-t6;
        float t8 = t7*t7;
        if (t8 > 1e-6f) {
            t8 = 1.0f/t8;
        } else {
            return;
        }
        float t11 = t2*t2;
        float t12 = t8*t11*4.0f;
        float t13 = t12+1.0f;
        float t14;
        if (fabsf(t13) > 1e-6f) {
            t14 = 1.0f/t13;
        } else {
            return;
        }

        H_YAW[0] = t8*t14*(q3*t3+q3*t4-q3*t5+q3*t6-q0*q1*q2*2.0f)*-2.0f;
        H_YAW[1] = t8*t14*(q2*t3+q2*t4+q2*t5-q2*t6-q0*q1*q3*2.0f)*-2.0f;
        H_YAW[2] = t8*t14*(-q1*t3+q1*t4+q1*t5+q1*t6-q0*q2*q3*2.0f)*2.0f;
        H_YAW[3] = t8*t14*(q0*t3-q0*t4+q0*t5+q0*t6-q1*q2*q3*2.0f)*2.0f;

        // Get the 321 euler angles
        Vector3f euler312 = stateStruct.quat.to_vector312();
        yawAngPredicted = euler312.z;

        // set the yaw to zero and calculate the zero yaw rotation from body to earth frame
        Tbn_zeroYaw.from_euler312(euler312.x, euler312.y, 0.0f);
    }

    // Calculate the innovation
    float innovation;
    if (!usePredictedYaw) {
        if (!useExternalYawSensor) {
            // Use the difference between the horizontal projection and declination to give the measured yaw
            // rotate measured mag components into earth frame
            Vector3f magMeasNED = Tbn_zeroYaw*magDataDelayed.mag;
            float yawAngMeasured = wrap_PI(-atan2f(magMeasNED.y, magMeasNED.x) + MagDeclination());
            innovation = wrap_PI(yawAngPredicted - yawAngMeasured);
        } else {
            // use the external yaw sensor data
            innovation = wrap_PI(yawAngPredicted - yawAngDataDelayed.yawAng);
        }
    } else if (canUseGsfYaw) {
        // The GSF yaw esitimator can provide a better estimate than the main nav filter can when no yaw
        // sensor is available
        innovation = wrap_PI(yawAngPredicted - gsfYaw);
    } else {
        // setting the innovation to zero enables quaternion covariance growth to be constrained when there
        // is no method of observing yaw
        innovation = 0.0f;
    }

    // Copy raw value to output variable used for data logging
    innovYaw = innovation;

    // Calculate innovation variance and Kalman gains, taking advantage of the fact that only the first 4 elements in H are non zero
    float PH[4];
    float varInnov = R_YAW;
    for (uint8_t rowIndex=0; rowIndex<=3; rowIndex++) {
        PH[rowIndex] = 0.0f;
        for (uint8_t colIndex=0; colIndex<=3; colIndex++) {
            PH[rowIndex] += P[rowIndex][colIndex]*H_YAW[colIndex];
        }
        varInnov += H_YAW[rowIndex]*PH[rowIndex];
    }
    float varInnovInv;
    if (varInnov >= R_YAW) {
        varInnovInv = 1.0f / varInnov;
        // output numerical health status
        faultStatus.bad_yaw = false;
    } else {
        // the calculation is badly conditioned, so we cannot perform fusion on this step
        // we reset the covariance matrix and try again next measurement
        CovarianceInit();
        // output numerical health status
        faultStatus.bad_yaw = true;
        return;
    }

    // calculate Kalman gain
    for (uint8_t rowIndex=0; rowIndex<=stateIndexLim; rowIndex++) {
        Kfusion[rowIndex] = 0.0f;
        for (uint8_t colIndex=0; colIndex<=3; colIndex++) {
            Kfusion[rowIndex] += P[rowIndex][colIndex]*H_YAW[colIndex];
        }
        Kfusion[rowIndex] *= varInnovInv;
    }

    // calculate the innovation test ratio
    yawTestRatio = sq(innovation) / (sq(MAX(0.01f * (float)frontend->_yawInnovGate, 1.0f)) * varInnov);

    // Declare the magnetometer unhealthy if the innovation test fails
    if (yawTestRatio > 1.0f) {
        magHealth = false;
        // On the ground a large innovation could be due to large initial gyro bias or magnetic interference from nearby objects
        // If we are flying, then it is more likely due to a magnetometer fault and we should not fuse the data
        if (inFlight) {
            return;
        }
    } else {
        magHealth = true;
    }

    // limit the innovation so that initial corrections are not too large
    if (innovation > 0.5f) {
        innovation = 0.5f;
    } else if (innovation < -0.5f) {
        innovation = -0.5f;
    }

    // correct the covariance using P = P - K*H*P taking advantage of the fact that only the first 3 elements in H are non zero
    // calculate K*H*P
    for (uint8_t row = 0; row <= stateIndexLim; row++) {
        for (uint8_t column = 0; column <= 3; column++) {
            KH[row][column] = Kfusion[row] * H_YAW[column];
        }
    }
    for (uint8_t row = 0; row <= stateIndexLim; row++) {
        for (uint8_t column = 0; column <= stateIndexLim; column++) {
            float tmp = KH[row][0] * P[0][column];
            tmp += KH[row][1] * P[1][column];
            tmp += KH[row][2] * P[2][column];
            tmp += KH[row][3] * P[3][column];
            KHP[row][column] = tmp;
        }
    }

    // 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 i=0; i<=stateIndexLim; i++) {
            statesArray[i] -= Kfusion[i] * innovation;
        }
        stateStruct.quat.normalize();

        // record fusion numerical health status
        faultStatus.bad_yaw = false;

    } else {
        // record fusion numerical health status
        faultStatus.bad_yaw = true;
    }
}

/*
 * Fuse declination angle 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
 * This is used to prevent the declination of the EKF earth field states from drifting during operation without GPS
 * or some other absolute position or velocity reference
*/
void NavEKF3_core::FuseDeclination(float declErr)
{
    // declination error variance (rad^2)
    const float R_DECL = sq(declErr);

    // copy required states to local variables
    float magN = stateStruct.earth_magfield.x;
    float magE = stateStruct.earth_magfield.y;

    // prevent bad earth field states from causing numerical errors or exceptions
    if (magN < 1e-3f) {
        return;
    }

    // Calculate observation Jacobian and Kalman gains
    // Calculate intermediate variables
    float t2 = magE*magE;
    float t3 = magN*magN;
    float t4 = t2+t3;
    // if the horizontal magnetic field is too small, this calculation will be badly conditioned
    if (t4 < 1e-4f) {
        return;
    }
    float t5 = P[16][16]*t2;
    float t6 = P[17][17]*t3;
    float t7 = t2*t2;
    float t8 = R_DECL*t7;
    float t9 = t3*t3;
    float t10 = R_DECL*t9;
    float t11 = R_DECL*t2*t3*2.0f;
    float t14 = P[16][17]*magE*magN;
    float t15 = P[17][16]*magE*magN;
    float t12 = t5+t6+t8+t10+t11-t14-t15;
    float t13;
    if (fabsf(t12) > 1e-6f) {
        t13 = 1.0f / t12;
    } else {
        return;
    }
    float t18 = magE*magE;
    float t19 = magN*magN;
    float t20 = t18+t19;
    float t21;
    if (fabsf(t20) > 1e-6f) {
        t21 = 1.0f/t20;
    } else {
        return;
    }

    // Calculate the observation Jacobian
    // Note only 2 terms are non-zero which can be used in matrix operations for calculation of Kalman gains and covariance update to significantly reduce cost
    float H_DECL[24] = {};
    H_DECL[16] = -magE*t21;
    H_DECL[17] = magN*t21;

    Kfusion[0] = -t4*t13*(P[0][16]*magE-P[0][17]*magN);
    Kfusion[1] = -t4*t13*(P[1][16]*magE-P[1][17]*magN);
    Kfusion[2] = -t4*t13*(P[2][16]*magE-P[2][17]*magN);
    Kfusion[3] = -t4*t13*(P[3][16]*magE-P[3][17]*magN);
    Kfusion[4] = -t4*t13*(P[4][16]*magE-P[4][17]*magN);
    Kfusion[5] = -t4*t13*(P[5][16]*magE-P[5][17]*magN);
    Kfusion[6] = -t4*t13*(P[6][16]*magE-P[6][17]*magN);
    Kfusion[7] = -t4*t13*(P[7][16]*magE-P[7][17]*magN);
    Kfusion[8] = -t4*t13*(P[8][16]*magE-P[8][17]*magN);
    Kfusion[9] = -t4*t13*(P[9][16]*magE-P[9][17]*magN);

    if (!inhibitDelAngBiasStates) {
        Kfusion[10] = -t4*t13*(P[10][16]*magE-P[10][17]*magN);
        Kfusion[11] = -t4*t13*(P[11][16]*magE-P[11][17]*magN);
        Kfusion[12] = -t4*t13*(P[12][16]*magE-P[12][17]*magN);
    } else {
        // zero indexes 10 to 12 = 3*4 bytes
        memset(&Kfusion[10], 0, 12);
    }

    if (!inhibitDelVelBiasStates) {
        Kfusion[13] = -t4*t13*(P[13][16]*magE-P[13][17]*magN);
        Kfusion[14] = -t4*t13*(P[14][16]*magE-P[14][17]*magN);
        Kfusion[15] = -t4*t13*(P[15][16]*magE-P[15][17]*magN);
    } else {
        // zero indexes 13 to 15 = 3*4 bytes
        memset(&Kfusion[13], 0, 12);
    }

    if (!inhibitMagStates) {
        Kfusion[16] = -t4*t13*(P[16][16]*magE-P[16][17]*magN);
        Kfusion[17] = -t4*t13*(P[17][16]*magE-P[17][17]*magN);
        Kfusion[18] = -t4*t13*(P[18][16]*magE-P[18][17]*magN);
        Kfusion[19] = -t4*t13*(P[19][16]*magE-P[19][17]*magN);
        Kfusion[20] = -t4*t13*(P[20][16]*magE-P[20][17]*magN);
        Kfusion[21] = -t4*t13*(P[21][16]*magE-P[21][17]*magN);
    } else {
        // zero indexes 16 to 21 = 6*4 bytes
        memset(&Kfusion[16], 0, 24);
    }

    if (!inhibitWindStates) {
        Kfusion[22] = -t4*t13*(P[22][16]*magE-P[22][17]*magN);
        Kfusion[23] = -t4*t13*(P[23][16]*magE-P[23][17]*magN);
    } else {
        // zero indexes 22 to 23 = 2*4 bytes
        memset(&Kfusion[22], 0, 8);
    }

    // get the magnetic declination
    float magDecAng = MagDeclination();

    // Calculate the innovation
    float innovation = atan2f(magE , magN) - magDecAng;

    // limit the innovation to protect against data errors
    if (innovation > 0.5f) {
        innovation = 0.5f;
    } else if (innovation < -0.5f) {
        innovation = -0.5f;
    }

    // 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<=15; j++) {
            KH[i][j] = 0.0f;
        }
        KH[i][16] = Kfusion[i] * H_DECL[16];
        KH[i][17] = Kfusion[i] * H_DECL[17];
        for (unsigned j = 18; j<=23; j++) {
            KH[i][j] = 0.0f;
        }
    }
    for (unsigned j = 0; j<=stateIndexLim; j++) {
        for (unsigned i = 0; i<=stateIndexLim; i++) {
            KHP[i][j] = KH[i][16] * P[16][j] + KH[i][17] * P[17][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();

        // correct the state vector
        for (uint8_t j= 0; j<=stateIndexLim; j++) {
            statesArray[j] = statesArray[j] - Kfusion[j] * innovation;
        }
        stateStruct.quat.normalize();

        // record fusion health status
        faultStatus.bad_decl = false;
    } else {
        // record fusion health status
        faultStatus.bad_decl = true;
    }
}

/********************************************************
*                   MISC FUNCTIONS                      *
********************************************************/

// align the NE earth magnetic field states with the published declination
void NavEKF3_core::alignMagStateDeclination()
{
    // don't do this if we already have a learned magnetic field
    if (magFieldLearned) {
        return;
    }

    // get the magnetic declination
    float magDecAng = MagDeclination();

    // rotate the NE values so that the declination matches the published value
    Vector3f initMagNED = stateStruct.earth_magfield;
    float magLengthNE = norm(initMagNED.x,initMagNED.y);
    stateStruct.earth_magfield.x = magLengthNE * cosf(magDecAng);
    stateStruct.earth_magfield.y = magLengthNE * sinf(magDecAng);

    if (!inhibitMagStates) {
        // zero the corresponding state covariances if magnetic field state learning is active
        float var_16 = P[16][16];
        float var_17 = P[17][17];
        zeroRows(P,16,17);
        zeroCols(P,16,17);
        P[16][16] = var_16;
        P[17][17] = var_17;

        // fuse the declination angle to establish covariances and prevent large swings in declination
        // during initial fusion
        FuseDeclination(0.1f);

    }
}

// record a magnetic field state reset event
void NavEKF3_core::recordMagReset()
{
    magStateResetRequest = false;
    magStateInitComplete = true;
    if (inFlight) {
        finalInflightMagInit = true;
    }
    // take a snap-shot of the vertical position, quaternion  and yaw innovation to use as a reference
    // for post alignment checks
    posDownAtLastMagReset = stateStruct.position.z;
    quatAtLastMagReset = stateStruct.quat;
    yawInnovAtLastMagReset = innovYaw;
}

/*
  learn magnetometer biases from GPS yaw. Return true if the
  resulting mag vector is close enough to the one predicted by GPS
  yaw to use it for fallback
*/
bool NavEKF3_core::learnMagBiasFromGPS(void)
{
    if (!have_table_earth_field) {
        // we need the earth field from WMM
        return false;
    }
    if (!inFlight) {
        // don't start learning till we've started flying
        return false;
    }

    mag_elements mag_data;
    if (!storedMag.recall(mag_data, imuDataDelayed.time_ms)) {
        // no mag data to correct
        return false;
    }

    // combine yaw with current quaternion to get yaw corrected quaternion
    Quaternion quat = stateStruct.quat;
    if (yawAngDataDelayed.type == 2) {
        Vector3f euler321;
        quat.to_euler(euler321.x, euler321.y, euler321.z);
        quat.from_euler(euler321.x, euler321.y, yawAngDataDelayed.yawAng);
    } else if (yawAngDataDelayed.type == 1) {
        Vector3f euler312 = quat.to_vector312();
        quat.from_vector312(euler312.x, euler312.y, yawAngDataDelayed.yawAng);
    }

    // build the expected body field from orientation and table earth field
    Matrix3f dcm;
    quat.rotation_matrix(dcm);
    Vector3f expected_body_field = dcm.transposed() * table_earth_field_ga;

    // calculate error in field
    Vector3f err = (expected_body_field - mag_data.mag) + stateStruct.body_magfield;

    // learn body frame mag biases
    stateStruct.body_magfield -= err * EK3_GPS_MAG_LEARN_RATE;

    // check if error is below threshold. If it is then we can
    // fallback to magnetometer on failure of external yaw
    float err_length = err.length();

    // we allow for yaw backback to compass if we have had 50 samples
    // in a row below the threshold. This corresponds to 10 seconds
    // for a 5Hz GPS
    const uint8_t fallback_count_threshold = 50;

    if (err_length > EK3_GPS_MAG_LEARN_LIMIT) {
        gps_yaw_fallback_good_counter = 0;
    } else if (gps_yaw_fallback_good_counter < fallback_count_threshold) {
        gps_yaw_fallback_good_counter++;
    }
    bool ok = gps_yaw_fallback_good_counter >= fallback_count_threshold;
    if (ok) {
        // mark mag healthy to prevent a magTimeout when we start using it
        lastHealthyMagTime_ms = imuSampleTime_ms;
    }
    return ok;
}

// Reset states using yaw from EKF-GSF and velocity and position from GPS
bool NavEKF3_core::EKFGSF_resetMainFilterYaw()
{
    // Don't do a reset unless permitted by the EK3_GSF_USE and EK3_GSF_RUN parameter masks
    if ((yawEstimator == nullptr)
        || !(frontend->_gsfUseMask & (1U<<core_index))
        || EKFGSF_yaw_reset_count >= frontend->_gsfResetMaxCount) {
        return false;
    };

    float yawEKFGSF, yawVarianceEKFGSF;
    if (yawEstimator->getYawData(yawEKFGSF, yawVarianceEKFGSF) && is_positive(yawVarianceEKFGSF) && yawVarianceEKFGSF < sq(radians(GSF_YAW_ACCURACY_THRESHOLD_DEG))) {

        // keep roll and pitch and reset yaw
        resetQuatStateYawOnly(yawEKFGSF, yawVarianceEKFGSF);

        // record the emergency reset event
        EKFGSF_yaw_reset_request_ms = 0;
        EKFGSF_yaw_reset_ms = imuSampleTime_ms;
        EKFGSF_yaw_reset_count++;

        if (!use_compass() || AP::compass().get_num_enabled() == 0) {
            gcs().send_text(MAV_SEVERITY_INFO, "EKF3 IMU%u yaw aligned using GPS",(unsigned)imu_index);
        } else {
            gcs().send_text(MAV_SEVERITY_WARNING, "EKF3 IMU%u emergency yaw reset",(unsigned)imu_index);
        }

        // Fail the magnetomer so it doesn't get used and pull the yaw away from the correct value
        allMagSensorsFailed = true;

        // record the yaw reset event
        recordYawReset();

        // reset velocity and position states to GPS - if yaw is fixed then the filter should start to operate correctly
        ResetVelocity(resetDataSource::DEFAULT);
        ResetPosition(resetDataSource::DEFAULT);

        // reset test ratios that are reported to prevent a race condition with the external state machine requesting the reset
        velTestRatio = 0.0f;
        posTestRatio = 0.0f;

        return true;

    }

    return false;

}

void NavEKF3_core::resetQuatStateYawOnly(float yaw, float yawVariance)
{
    Quaternion quatBeforeReset = stateStruct.quat;
    Vector3f angleErrVarVec = calcRotVecVariances();

    // check if we should use a 321 or 312 Rotation sequence and update the quaternion
    // states using the preferred yaw definition
    stateStruct.quat.inverse().rotation_matrix(prevTnb);
    Vector3f eulerAngles;
    if (fabsf(prevTnb[2][0]) < fabsf(prevTnb[2][1])) {
        // rolled more than pitched so use 321 rotation order
        stateStruct.quat.to_euler(eulerAngles.x, eulerAngles.y, eulerAngles.z);
        stateStruct.quat.from_euler(eulerAngles.x, eulerAngles.y, yaw);
    } else {
        // pitched more than rolled so use 312 rotation order
        eulerAngles = stateStruct.quat.to_vector312();
        stateStruct.quat.from_vector312(eulerAngles.x, eulerAngles.y, yaw);
    }

    // Update the rotation matrix
    stateStruct.quat.inverse().rotation_matrix(prevTnb);
    
    float deltaYaw = wrap_PI(yaw - eulerAngles.z);

    // calculate the change in the quaternion state and apply it to the output history buffer
    Quaternion quat_delta = stateStruct.quat / quatBeforeReset;
    StoreQuatRotate(quat_delta);

    // update the yaw angle variance using the variance of the EKF-GSF estimate
    angleErrVarVec.z = yawVariance;
    zeroRows(P,0,3);
    zeroCols(P,0,3);
    initialiseQuatCovariances(angleErrVarVec);

    // record the yaw reset event
    yawResetAngle += deltaYaw;
    lastYawReset_ms = imuSampleTime_ms;

    // record the yaw reset event
    recordYawReset();

    // clear all pending yaw reset requests
    gpsYawResetRequest = false;
    magYawResetRequest = false;
    
}