/// -*- tab-width: 4; Mode: C++; c-basic-offset: 4; indent-tabs-mode: nil -*-

#include <AP_HAL/AP_HAL.h>

#if HAL_CPU_CLASS >= HAL_CPU_CLASS_150

#include "AP_NavEKF2.h"
#include "AP_NavEKF2_core.h"
#include <AP_AHRS/AP_AHRS.h>
#include <AP_Vehicle/AP_Vehicle.h>

#include <stdio.h>

extern const AP_HAL::HAL& hal;

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

// Reset velocity states to last GPS measurement if available or to zero if in constant position mode or if PV aiding is not absolute
// Do not reset vertical velocity using GPS as there is baro alt available to constrain drift
void 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 if (!gpsNotAvailable) {
        // 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);

}

// 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 if (!gpsNotAvailable) {
        // 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 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);

}

// Reset the baro so that it reads zero at the current height
// Reset the EKF height to zero
// Adjust the EKf origin height so that the EKF height + origin height is the same as before
// Return true if the height datum reset has been performed
// If using a range finder for height do not reset and return false
bool NavEKF2_core::resetHeightDatum(void)
{
    // if we are using a range finder for height, return false
    if (frontend->_altSource == 1) {
        return false;
    }
    // record the old height estimate
    float oldHgt = -stateStruct.position.z;
    // reset the barometer so that it reads zero at the current height
    frontend->_baro.update_calibration();
    // reset the height state
    stateStruct.position.z = 0.0f;
    // adjust the height of the EKF origin so that the origin plus baro height before and afer the reset is the same
    if (validOrigin) {
        EKF_origin.alt += oldHgt*100;
    }
    return true;
}

/********************************************************
*                   FUSE MEASURED_DATA                  *
********************************************************/
// select fusion of velocity, position and height measurements
void NavEKF2_core::SelectVelPosFusion()
{
    // Check if the magnetometer has been fused on that time step and the filter is running at faster than 200 Hz
    // If so, don't fuse measurements on this time step to reduce frame over-runs
    // Only allow one time slip to prevent high rate magnetometer data preventing fusion of other measurements
    if (magFusePerformed && dtIMUavg < 0.005f && !posVelFusionDelayed) {
        posVelFusionDelayed = true;
        return;
    } else {
        posVelFusionDelayed = false;
    }

    // read GPS data from the sensor and check for new data in the buffer
    readGpsData();
    gpsDataToFuse = storedGPS.recall(gpsDataDelayed,imuDataDelayed.time_ms);
    // Determine if we need to fuse position and velocity data on this time step
    if (gpsDataToFuse && PV_AidingMode == AID_ABSOLUTE) {
        // Don't fuse velocity data if GPS doesn't support it
        if (frontend->_fusionModeGPS <= 1) {
            fuseVelData = true;
        } else {
            fuseVelData = false;
        }
        fusePosData = true;
    } else {
        fuseVelData = false;
        fusePosData = false;
    }

    // we have GPS data to fuse and a request to align the yaw using the GPS course
    if (gpsYawResetRequest) {
        realignYawGPS();
    }

    // Select height data to be fused from the available baro, range finder and GPS sources
    selectHeightForFusion();

    // If we are operating without any aiding, fuse in the last known position
    // to constrain tilt drift. This assumes a non-manoeuvring vehicle
    // Do this to coincide with the height fusion
    if (fuseHgtData && PV_AidingMode == AID_NONE) {
        gpsDataDelayed.vel.zero();
        gpsDataDelayed.pos.x = lastKnownPositionNE.x;
        gpsDataDelayed.pos.y = lastKnownPositionNE.y;

        fusePosData = true;
        fuseVelData = false;
    }

    // perform fusion
    if (fuseVelData || fusePosData || fuseHgtData) {
        FuseVelPosNED();
        // clear the flags to prevent repeated fusion of the same data
        fuseVelData = false;
        fuseHgtData = false;
        fusePosData = false;
    }
}

// fuse selected position, velocity and height measurements
void 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
    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
        float posErr = frontend->gpsPosVarAccScale * accNavMag;

        // estimate the GPS Velocity, GPS horiz position and height measurement variances.
        // Use different errors if operating without external aiding using an assumed position or velocity of zero
        if (PV_AidingMode == AID_NONE) {
            if (tiltAlignComplete && motorsArmed) {
            // This is a compromise between corrections for gyro errors and reducing effect of manoeuvre accelerations on tilt estimate
                R_OBS[0] = sq(constrain_float(frontend->_noaidHorizNoise, 0.5f, 50.0f));
            } else {
                // Use a smaller value to give faster initial alignment
                R_OBS[0] = sq(0.5f);
            }
            R_OBS[1] = R_OBS[0];
            R_OBS[2] = R_OBS[0];
            R_OBS[3] = R_OBS[0];
            R_OBS[4] = R_OBS[0];
            for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];
        } else {
            if (gpsSpdAccuracy > 0.0f) {
                // use GPS receivers reported speed accuracy if available and floor at value set by GPS velocity noise parameter
                R_OBS[0] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsHorizVelNoise, 50.0f));
                R_OBS[2] = sq(constrain_float(gpsSpdAccuracy, frontend->_gpsVertVelNoise, 50.0f));
            } else {
                // calculate additional error in GPS velocity caused by manoeuvring
                R_OBS[0] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
                R_OBS[2] = sq(constrain_float(frontend->_gpsVertVelNoise,  0.05f, 5.0f)) + sq(frontend->gpsDVelVarAccScale  * accNavMag);
            }
            R_OBS[1] = R_OBS[0];
            // Use GPS reported position accuracy if available and floor at value set by GPS position noise parameter
            if (gpsPosAccuracy > 0.0f) {
                R_OBS[3] = sq(constrain_float(gpsPosAccuracy, frontend->_gpsHorizPosNoise, 100.0f));
            } else {
                R_OBS[3] = sq(constrain_float(frontend->_gpsHorizPosNoise, 0.1f, 10.0f)) + sq(posErr);
            }
            R_OBS[4] = R_OBS[3];
            // For data integrity checks we use the same measurement variances as used to calculate the Kalman gains for all measurements except GPS horizontal velocity
            // For horizontal GPs velocity we don't want the acceptance radius to increase with reported GPS accuracy so we use a value based on best GPs perfomrance
            // plus a margin for manoeuvres. It is better to reject GPS horizontal velocity errors early
            for (uint8_t i=0; i<=2; i++) R_OBS_DATA_CHECKS[i] = sq(constrain_float(frontend->_gpsHorizVelNoise, 0.05f, 5.0f)) + sq(frontend->gpsNEVelVarAccScale * accNavMag);
        }
        R_OBS[5] = posDownObsNoise;
        for (uint8_t i=3; i<=5; i++) R_OBS_DATA_CHECKS[i] = R_OBS[i];

        // if vertical GPS velocity data and an independent height source is being used, check to see if the GPS vertical velocity and altimeter
        // innovations have the same sign and are outside limits. If so, then it is likely aliasing is affecting
        // the accelerometers and we should disable the GPS and barometer innovation consistency checks.
        if (useGpsVertVel && fuseVelData && (frontend->_altSource != 2)) {
            // calculate innovations for height and vertical GPS vel measurements
            float hgtErr  = stateStruct.position.z - observation[5];
            float velDErr = stateStruct.velocity.z - observation[2];
            // check if they are the same sign and both more than 3-sigma out of bounds
            if ((hgtErr*velDErr > 0.0f) && (sq(hgtErr) > 9.0f * (P[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 or timed out
            if (PV_AidingMode == AID_NONE) {
                posHealth = true;
                lastPosPassTime_ms = imuSampleTime_ms;
            } else if (posHealth || posTimeout) {
                posHealth = true;
                lastPosPassTime_ms = imuSampleTime_ms;
                // if timed out or outside the specified uncertainty radius, reset to the GPS
                if (posTimeout || ((P[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;
                }

                // update the covariance - take advantage of direct observation of a single state at index = stateIndex to reduce computations
                // this is a numerically optimised implementation of standard equation P = (I - K*H)*P;
                for (uint8_t i= 0; i<=stateIndexLim; i++) {
                    for (uint8_t j= 0; j<=stateIndexLim; j++)
                    {
                        KHP[i][j] = Kfusion[i] * P[stateIndex][j];
                    }
                }
                // Check that we are not going to drive any variances negative and skip the update if so
                bool healthyFusion = true;
                for (uint8_t i= 0; i<=stateIndexLim; i++) {
                    if (KHP[i][i] > P[i][i]) {
                        healthyFusion = false;
                    }
                }
                if (healthyFusion) {
                    // update the covariance matrix
                    for (uint8_t i= 0; i<=stateIndexLim; i++) {
                        for (uint8_t j= 0; j<=stateIndexLim; j++) {
                            P[i][j] = P[i][j] - KHP[i][j];
                        }
                    }

                    // force the covariance matrix to be symmetrical and limit the variances to prevent ill-condiioning.
                    ForceSymmetry();
                    ConstrainVariances();

                    // update the states
                    // 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;
                    }
                    // record good fusion status
                    if (obsIndex == 0) {
                        faultStatus.bad_nvel = false;
                    } else if (obsIndex == 1) {
                        faultStatus.bad_evel = false;
                    } else if (obsIndex == 2) {
                        faultStatus.bad_dvel = false;
                    } else if (obsIndex == 3) {
                        faultStatus.bad_npos = false;
                    } else if (obsIndex == 4) {
                        faultStatus.bad_epos = false;
                    } else if (obsIndex == 5) {
                        faultStatus.bad_dpos = false;
                    }
                } else {
                    // record bad fusion status
                    if (obsIndex == 0) {
                        faultStatus.bad_nvel = true;
                    } else if (obsIndex == 1) {
                        faultStatus.bad_evel = true;
                    } else if (obsIndex == 2) {
                        faultStatus.bad_dvel = true;
                    } else if (obsIndex == 3) {
                        faultStatus.bad_npos = true;
                    } else if (obsIndex == 4) {
                        faultStatus.bad_epos = true;
                    } else if (obsIndex == 5) {
                        faultStatus.bad_dpos = true;
                    }
                }
            }
        }
    }

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

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

// select the height measurement to be fused from the available baro, range finder and GPS sources
void NavEKF2_core::selectHeightForFusion()
{
    // Read range finder data and check for new data in the buffer
    // This data is used by both height and optical flow fusion processing
    readRangeFinder();
    rangeDataToFuse = storedRange.recall(rangeDataDelayed,imuDataDelayed.time_ms);

    // read baro height data from the sensor and check for new data in the buffer
    readBaroData();
    baroDataToFuse = storedBaro.recall(baroDataDelayed, imuDataDelayed.time_ms);

    // determine if we should be using a height source other than baro
    bool usingRangeForHgt = (frontend->_altSource == 1 && imuSampleTime_ms - rngValidMeaTime_ms < 500 && frontend->_fusionModeGPS == 3);
    bool usingGpsForHgt = (frontend->_altSource == 2 && imuSampleTime_ms - lastTimeGpsReceived_ms < 500 && validOrigin && gpsAccuracyGood);

    // if there is new baro data to fuse, calculate filterred baro data required by other processes
    if (baroDataToFuse) {
        // calculate offset to baro data that enables baro to be used as a backup if we are using other height sources
        if  (usingRangeForHgt || usingGpsForHgt) {
            calcFiltBaroOffset();
        }
        // filtered baro data used to provide a reference for takeoff
        // it is is reset to last height measurement on disarming in performArmingChecks()
        if (!getTakeoffExpected()) {
            const float gndHgtFiltTC = 0.5f;
            const float dtBaro = frontend->hgtAvg_ms*1.0e-3f;
            float alpha = constrain_float(dtBaro / (dtBaro+gndHgtFiltTC),0.0f,1.0f);
            meaHgtAtTakeOff += (baroDataDelayed.hgt-meaHgtAtTakeOff)*alpha;
        }
    }

    // Select the height measurement source
    if (rangeDataToFuse && usingRangeForHgt) {
        // using range finder data
        // correct for tilt using a flat earth model
        if (prevTnb.c.z >= 0.7) {
            hgtMea  = MAX(rangeDataDelayed.rng * prevTnb.c.z, rngOnGnd);
            // enable fusion
            fuseHgtData = true;
            // set the observation noise
            posDownObsNoise = sq(constrain_float(frontend->_rngNoise, 0.1f, 10.0f));
        } else {
            // disable fusion if tilted too far
            fuseHgtData = false;
        }
    } else if  (gpsDataToFuse && usingGpsForHgt) {
        // using GPS data
        hgtMea = gpsDataDelayed.hgt;
        // enable fusion
        fuseHgtData = true;
        // set the observation noise to the horizontal GPS noise plus a scaler because GPS vertical position is usually less accurate
        // TODO use VDOP/HDOP, reported accuracy or a separate parameter
        posDownObsNoise = sq(constrain_float(frontend->_gpsHorizPosNoise * 1.5f, 0.1f, 10.0f));
    } else if (baroDataToFuse && !usingRangeForHgt && !usingGpsForHgt) {
        // using Baro data
        hgtMea = baroDataDelayed.hgt - baroHgtOffset;
        // enable fusion
        fuseHgtData = true;
        // set the observation noise
        posDownObsNoise = sq(constrain_float(frontend->_baroAltNoise, 0.1f, 10.0f));
        // reduce weighting (increase observation noise) on baro if we are likely to be in ground effect
        if (getTakeoffExpected() || getTouchdownExpected()) {
            posDownObsNoise *= frontend->gndEffectBaroScaler;
        }
    } else {
        fuseHgtData = false;
    }

    // If we haven't fused height data for a while, then declare the height data as being timed out
    // set timeout period based on whether we have vertical GPS velocity available to constrain drift
    hgtRetryTime_ms = (useGpsVertVel && !velTimeout) ? frontend->hgtRetryTimeMode0_ms : frontend->hgtRetryTimeMode12_ms;
    if (imuSampleTime_ms - lastHgtPassTime_ms > hgtRetryTime_ms) {
        hgtTimeout = true;
    } else {
        hgtTimeout = false;
    }
}

#endif // HAL_CPU_CLASS
