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/**
 * @file airspeed_fusion.cpp
 * airspeed fusion methods.
 *
 * @author Carl Olsson <carlolsson.co@gmail.com>
 * @author Roman Bast <bapstroman@gmail.com>
 * @author Paul Riseborough <p_riseborough@live.com.au>
 *
 */
#include "../ecl.h"
#include "ekf.h"
#include <mathlib/mathlib.h>

void Ekf::fuseAirspeed()
{
	float SH_TAS[3] = {}; // Variable used to optimise calculations of measurement jacobian
	float H_TAS[24] = {}; // Observation Jacobian
	float SK_TAS[2] = {}; // Variable used to optimise calculations of the Kalman gain vector
	float Kfusion[24] = {}; // Kalman gain vector

	const float vn = _state.vel(0); // Velocity in north direction
	const float ve = _state.vel(1); // Velocity in east direction
	const float vd = _state.vel(2); // Velocity in downwards direction
	const float vwn = _state.wind_vel(0); // Wind speed in north direction
	const float vwe = _state.wind_vel(1); // Wind speed in east direction

	// Calculate the predicted airspeed
	const float v_tas_pred = sqrtf((ve - vwe) * (ve - vwe) + (vn - vwn) * (vn - vwn) + vd * vd);

	// Variance for true airspeed measurement - (m/sec)^2
	const float R_TAS = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) *
			    math::constrain(_airspeed_sample_delayed.eas2tas, 0.9f, 10.0f));

	// Perform fusion of True Airspeed measurement
	if (v_tas_pred > 1.0f) {
		// determine if we need the sideslip fusion to correct states other than wind
		const bool update_wind_only = !_is_wind_dead_reckoning;

		// Calculate the observation jacobian
		// intermediate variable from algebraic optimisation
		SH_TAS[0] = 1.0f/v_tas_pred;
		SH_TAS[1] = (SH_TAS[0]*(2.0f*ve - 2.0f*vwe))*0.5f;
		SH_TAS[2] = (SH_TAS[0]*(2.0f*vn - 2.0f*vwn))*0.5f;

		for (uint8_t i = 0; i < _k_num_states; i++) { H_TAS[i] = 0.0f; }

		H_TAS[4] = SH_TAS[2];
		H_TAS[5] = SH_TAS[1];
		H_TAS[6] = vd*SH_TAS[0];
		H_TAS[22] = -SH_TAS[2];
		H_TAS[23] = -SH_TAS[1];

		// We don't want to update the innovation variance if the calculation is ill conditioned
		const float _airspeed_innov_var_temp = (R_TAS + SH_TAS[2]*(P(4,4)*SH_TAS[2] + P(5,4)*SH_TAS[1] - P(22,4)*SH_TAS[2] - P(23,4)*SH_TAS[1] + P(6,4)*vd*SH_TAS[0]) + SH_TAS[1]*(P(4,5)*SH_TAS[2] + P(5,5)*SH_TAS[1] - P(22,5)*SH_TAS[2] - P(23,5)*SH_TAS[1] + P(6,5)*vd*SH_TAS[0]) - SH_TAS[2]*(P(4,22)*SH_TAS[2] + P(5,22)*SH_TAS[1] - P(22,22)*SH_TAS[2] - P(23,22)*SH_TAS[1] + P(6,22)*vd*SH_TAS[0]) - SH_TAS[1]*(P(4,23)*SH_TAS[2] + P(5,23)*SH_TAS[1] - P(22,23)*SH_TAS[2] - P(23,23)*SH_TAS[1] + P(6,23)*vd*SH_TAS[0]) + vd*SH_TAS[0]*(P(4,6)*SH_TAS[2] + P(5,6)*SH_TAS[1] - P(22,6)*SH_TAS[2] - P(23,6)*SH_TAS[1] + P(6,6)*vd*SH_TAS[0]));

		if (_airspeed_innov_var_temp >= R_TAS) { // Check for badly conditioned calculation
			SK_TAS[0] = 1.0f / _airspeed_innov_var_temp;
			_fault_status.flags.bad_airspeed = false;

		} else { // Reset the estimator covariance matrix
			_fault_status.flags.bad_airspeed = true;

			// if we are getting aiding from other sources, warn and reset the wind states and covariances only
			if (update_wind_only) {
				resetWindStates();
				resetWindCovariance();
				ECL_ERR_TIMESTAMPED("airspeed fusion badly conditioned - wind covariance reset");

			} else {
				initialiseCovariance();
				_state.wind_vel.setZero();
				ECL_ERR_TIMESTAMPED("airspeed fusion badly conditioned - full covariance reset");
			}

			return;
		}

		SK_TAS[1] = SH_TAS[1];

		if (update_wind_only) {
			// If we are getting aiding from other sources, then don't allow the airspeed measurements to affect the non-windspeed states
			for (unsigned row = 0; row <= 21; row++) {
				Kfusion[row] = 0.0f;
			}

		} else {
			// we have no other source of aiding, so use airspeed measurements to correct states
			Kfusion[0] = SK_TAS[0]*(P(0,4)*SH_TAS[2] - P(0,22)*SH_TAS[2] + P(0,5)*SK_TAS[1] - P(0,23)*SK_TAS[1] + P(0,6)*vd*SH_TAS[0]);
			Kfusion[1] = SK_TAS[0]*(P(1,4)*SH_TAS[2] - P(1,22)*SH_TAS[2] + P(1,5)*SK_TAS[1] - P(1,23)*SK_TAS[1] + P(1,6)*vd*SH_TAS[0]);
			Kfusion[2] = SK_TAS[0]*(P(2,4)*SH_TAS[2] - P(2,22)*SH_TAS[2] + P(2,5)*SK_TAS[1] - P(2,23)*SK_TAS[1] + P(2,6)*vd*SH_TAS[0]);
			Kfusion[3] = SK_TAS[0]*(P(3,4)*SH_TAS[2] - P(3,22)*SH_TAS[2] + P(3,5)*SK_TAS[1] - P(3,23)*SK_TAS[1] + P(3,6)*vd*SH_TAS[0]);
			Kfusion[4] = SK_TAS[0]*(P(4,4)*SH_TAS[2] - P(4,22)*SH_TAS[2] + P(4,5)*SK_TAS[1] - P(4,23)*SK_TAS[1] + P(4,6)*vd*SH_TAS[0]);
			Kfusion[5] = SK_TAS[0]*(P(5,4)*SH_TAS[2] - P(5,22)*SH_TAS[2] + P(5,5)*SK_TAS[1] - P(5,23)*SK_TAS[1] + P(5,6)*vd*SH_TAS[0]);
			Kfusion[6] = SK_TAS[0]*(P(6,4)*SH_TAS[2] - P(6,22)*SH_TAS[2] + P(6,5)*SK_TAS[1] - P(6,23)*SK_TAS[1] + P(6,6)*vd*SH_TAS[0]);
			Kfusion[7] = SK_TAS[0]*(P(7,4)*SH_TAS[2] - P(7,22)*SH_TAS[2] + P(7,5)*SK_TAS[1] - P(7,23)*SK_TAS[1] + P(7,6)*vd*SH_TAS[0]);
			Kfusion[8] = SK_TAS[0]*(P(8,4)*SH_TAS[2] - P(8,22)*SH_TAS[2] + P(8,5)*SK_TAS[1] - P(8,23)*SK_TAS[1] + P(8,6)*vd*SH_TAS[0]);
			Kfusion[9] = SK_TAS[0]*(P(9,4)*SH_TAS[2] - P(9,22)*SH_TAS[2] + P(9,5)*SK_TAS[1] - P(9,23)*SK_TAS[1] + P(9,6)*vd*SH_TAS[0]);
			Kfusion[10] = SK_TAS[0]*(P(10,4)*SH_TAS[2] - P(10,22)*SH_TAS[2] + P(10,5)*SK_TAS[1] - P(10,23)*SK_TAS[1] + P(10,6)*vd*SH_TAS[0]);
			Kfusion[11] = SK_TAS[0]*(P(11,4)*SH_TAS[2] - P(11,22)*SH_TAS[2] + P(11,5)*SK_TAS[1] - P(11,23)*SK_TAS[1] + P(11,6)*vd*SH_TAS[0]);
			Kfusion[12] = SK_TAS[0]*(P(12,4)*SH_TAS[2] - P(12,22)*SH_TAS[2] + P(12,5)*SK_TAS[1] - P(12,23)*SK_TAS[1] + P(12,6)*vd*SH_TAS[0]);
			Kfusion[13] = SK_TAS[0]*(P(13,4)*SH_TAS[2] - P(13,22)*SH_TAS[2] + P(13,5)*SK_TAS[1] - P(13,23)*SK_TAS[1] + P(13,6)*vd*SH_TAS[0]);
			Kfusion[14] = SK_TAS[0]*(P(14,4)*SH_TAS[2] - P(14,22)*SH_TAS[2] + P(14,5)*SK_TAS[1] - P(14,23)*SK_TAS[1] + P(14,6)*vd*SH_TAS[0]);
			Kfusion[15] = SK_TAS[0]*(P(15,4)*SH_TAS[2] - P(15,22)*SH_TAS[2] + P(15,5)*SK_TAS[1] - P(15,23)*SK_TAS[1] + P(15,6)*vd*SH_TAS[0]);
			Kfusion[16] = SK_TAS[0]*(P(16,4)*SH_TAS[2] - P(16,22)*SH_TAS[2] + P(16,5)*SK_TAS[1] - P(16,23)*SK_TAS[1] + P(16,6)*vd*SH_TAS[0]);
			Kfusion[17] = SK_TAS[0]*(P(17,4)*SH_TAS[2] - P(17,22)*SH_TAS[2] + P(17,5)*SK_TAS[1] - P(17,23)*SK_TAS[1] + P(17,6)*vd*SH_TAS[0]);
			Kfusion[18] = SK_TAS[0]*(P(18,4)*SH_TAS[2] - P(18,22)*SH_TAS[2] + P(18,5)*SK_TAS[1] - P(18,23)*SK_TAS[1] + P(18,6)*vd*SH_TAS[0]);
			Kfusion[19] = SK_TAS[0]*(P(19,4)*SH_TAS[2] - P(19,22)*SH_TAS[2] + P(19,5)*SK_TAS[1] - P(19,23)*SK_TAS[1] + P(19,6)*vd*SH_TAS[0]);
			Kfusion[20] = SK_TAS[0]*(P(20,4)*SH_TAS[2] - P(20,22)*SH_TAS[2] + P(20,5)*SK_TAS[1] - P(20,23)*SK_TAS[1] + P(20,6)*vd*SH_TAS[0]);
			Kfusion[21] = SK_TAS[0]*(P(21,4)*SH_TAS[2] - P(21,22)*SH_TAS[2] + P(21,5)*SK_TAS[1] - P(21,23)*SK_TAS[1] + P(21,6)*vd*SH_TAS[0]);
		}
		Kfusion[22] = SK_TAS[0]*(P(22,4)*SH_TAS[2] - P(22,22)*SH_TAS[2] + P(22,5)*SK_TAS[1] - P(22,23)*SK_TAS[1] + P(22,6)*vd*SH_TAS[0]);
		Kfusion[23] = SK_TAS[0]*(P(23,4)*SH_TAS[2] - P(23,22)*SH_TAS[2] + P(23,5)*SK_TAS[1] - P(23,23)*SK_TAS[1] + P(23,6)*vd*SH_TAS[0]);


		// Calculate measurement innovation
		_airspeed_innov = v_tas_pred -
				  _airspeed_sample_delayed.true_airspeed;

		// Calculate the innovation variance
		_airspeed_innov_var = 1.0f / SK_TAS[0];

		// Compute the ratio of innovation to gate size
		_tas_test_ratio = sq(_airspeed_innov) / (sq(fmaxf(_params.tas_innov_gate, 1.0f)) * _airspeed_innov_var);

		// If the innovation consistency check fails then don't fuse the sample and indicate bad airspeed health
		if (_tas_test_ratio > 1.0f) {
			_innov_check_fail_status.flags.reject_airspeed = true;
			return;

		} else {
			_innov_check_fail_status.flags.reject_airspeed = false;
		}

		// Airspeed measurement sample has passed check so record it
		_time_last_arsp_fuse = _time_last_imu;

		// apply covariance correction via P_new = (I -K*H)*P
		// first calculate expression for KHP
		// then calculate P - KHP
		matrix::SquareMatrix<float, _k_num_states> KHP;
		float KH[5];

		for (unsigned row = 0; row < _k_num_states; row++) {

			KH[0] = Kfusion[row] * H_TAS[4];
			KH[1] = Kfusion[row] * H_TAS[5];
			KH[2] = Kfusion[row] * H_TAS[6];
			KH[3] = Kfusion[row] * H_TAS[22];
			KH[4] = Kfusion[row] * H_TAS[23];

			for (unsigned column = 0; column < _k_num_states; column++) {
				float tmp = KH[0] * P(4,column);
				tmp += KH[1] * P(5,column);
				tmp += KH[2] * P(6,column);
				tmp += KH[3] * P(22,column);
				tmp += KH[4] * P(23,column);
				KHP(row,column) = tmp;
			}
		}

		// if the covariance correction will result in a negative variance, then
		// the covariance matrix is unhealthy and must be corrected
		bool healthy = true;
		_fault_status.flags.bad_airspeed = false;

		for (int i = 0; i < _k_num_states; i++) {
			if (P(i,i) < KHP(i,i)) {
				// zero rows and columns
				P.uncorrelateCovarianceSetVariance<1>(i, 0.0f);

				//flag as unhealthy
				healthy = false;

				// update individual measurement health status
				_fault_status.flags.bad_airspeed = true;

			}
		}

		// only apply covariance and state corrections if healthy
		if (healthy) {
			// apply the covariance corrections
			P = P - KHP;

			// correct the covariance matrix for gross errors
			fixCovarianceErrors(true);

			// apply the state corrections
			fuse(Kfusion, _airspeed_innov);

		}
	}
}

Vector2f Ekf::getWindVelocity() const
{
	return _state.wind_vel;
}

Vector2f Ekf::getWindVelocityVariance() const
{
	return P.slice<2, 2>(22,22).diag();
}

void Ekf::get_true_airspeed(float *tas)
{
	float tempvar = sqrtf(sq(_state.vel(0) - _state.wind_vel(0)) + sq(_state.vel(1) - _state.wind_vel(1)) + sq(_state.vel(2)));
	memcpy(tas, &tempvar, sizeof(float));
}

/*
 * Reset the wind states using the current airspeed measurement, ground relative nav velocity, yaw angle and assumption of zero sideslip
*/
void Ekf::resetWindStates()
{
	const Eulerf euler321(_state.quat_nominal);
	const float euler_yaw = euler321(2);

	if (_tas_data_ready && (_imu_sample_delayed.time_us - _airspeed_sample_delayed.time_us < (uint64_t)5e5)) {
		// estimate wind using zero sideslip assumption and airspeed measurement if airspeed available
		_state.wind_vel(0) = _state.vel(0) - _airspeed_sample_delayed.true_airspeed * cosf(euler_yaw);
		_state.wind_vel(1) = _state.vel(1) - _airspeed_sample_delayed.true_airspeed * sinf(euler_yaw);

	} else {
		// If we don't have an airspeed measurement, then assume the wind is zero
		_state.wind_vel.setZero();
	}
}
