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/**
 * @file vel_pos_fusion.cpp
 * Function for fusing gps and baro measurements/
 *
 * @author Roman Bast <bapstroman@gmail.com>
 *
 */

#include "ekf.h"

void Ekf::fuseVelPosHeight()
{
    bool fuse_map[6] = {}; // map of booelans true when [VN,VE,VD,PN,PE,PD] observations are available
    bool innov_check_pass_map[6] = {}; // true when innovations consistency checks pass for [VN,VE,VD,PN,PE,PD] observations
    float R[6] = {}; // observation variances for [VN,VE,VD,PN,PE,PD]
    float gate_size[6] = {}; // innovation consistency check gate sizes for [VN,VE,VD,PN,PE,PD] observations
    float Kfusion[24] = {}; // Kalman gain vector for any single observation - sequential fusion is used

    // calculate innovations, innovations gate sizes and observation variances
	if (_fuse_hor_vel) {
		fuse_map[0] = fuse_map[1] = true;
        // horizontal velocity innovations
		_vel_pos_innov[0] = _state.vel(0) - _gps_sample_delayed.vel(0);
		_vel_pos_innov[1] = _state.vel(1) - _gps_sample_delayed.vel(1);
        // observation variance - use receiver reported accuracy with parameter setting the minimum value
        R[0] = fmaxf(_params.gps_vel_noise, 0.01f);
        R[0] = fmaxf(R[0], _gps_speed_accuracy);
        R[0] = R[0] * R[0];
        R[1] = R[0];
        // innovation gate sizes
        gate_size[0] = fmaxf(_params.vel_innov_gate, 1.0f);
        gate_size[1] = gate_size[0];
    }

	if (_fuse_vert_vel) {
		fuse_map[2] = true;
        // vertical velocity innovation
        _vel_pos_innov[2] = _state.vel(2) - _gps_sample_delayed.vel(2);
        // observation variance - use receiver reported accuracy with parameter setting the minimum value
        R[2] = fmaxf(_params.gps_vel_noise, 0.01f);
        // use scaled horizontal speed accuracy assuming typical ratio of VDOP/HDOP
        R[2] = 1.5f * fmaxf(R[2], _gps_speed_accuracy);
        R[2] = R[2] * R[2];
        // innovation gate size
        gate_size[2] = fmaxf(_params.vel_innov_gate, 1.0f);
    }

	if (_fuse_pos) {
		fuse_map[3] = fuse_map[4] = true;
        // horizontal position innovations
		_vel_pos_innov[3] = _state.pos(0) - _gps_sample_delayed.pos(0);
		_vel_pos_innov[4] = _state.pos(1) - _gps_sample_delayed.pos(1);
        // observation variance - user parameter defined
        // if we are in flight and not using GPS, then use a specific parameter
        if (!_control_status.flags.gps && _control_status.flags.in_air) {
            R[3] = fmaxf(_params.pos_noaid_noise, 0.5f);
        } else {
            R[3] = fmaxf(_params.gps_pos_noise, 0.01f);
        }
        R[3] = R[3] * R[3];
        R[4] = R[3];
        // innovation gate sizes
        gate_size[3] = fmaxf(_params.posNE_innov_gate, 1.0f);
        gate_size[4] = gate_size[3];
	}

	if (_fuse_height) {
		fuse_map[5] = true;
        // vertical position innovation - baro measurement has opposite sign to earth z axis
        _vel_pos_innov[5] = _state.pos(2) - (-_baro_sample_delayed.hgt);
        // observation variance - user parameter defined
        R[5] = fmaxf(_params.baro_noise, 0.01f);
        R[5] = R[5] * R[5];
        // innovation gate size
        gate_size[5] = fmaxf(_params.baro_innov_gate, 1.0f);
    }

    // calculate innovation test ratios
    for (unsigned obs_index = 0; obs_index < 6; obs_index++) {
        if (fuse_map[obs_index]) {
            // compute the innovation variance SK = HPH + R
            unsigned state_index = obs_index + 3;	// we start with vx and this is the 4. state
            _vel_pos_innov_var[obs_index] = P[state_index][state_index] + R[obs_index];
            // Compute the ratio of innovation to gate size
            _vel_pos_test_ratio[obs_index] = sq(_vel_pos_innov[obs_index]) / (sq(gate_size[obs_index]) * _vel_pos_innov[obs_index]);
        }
    }

    // check position, velocity and height innovations
    // treat 3D velocity, 2D position and height as separate sensors
    // always pass position checks if using synthetic position measurements
    bool vel_check_pass = (_vel_pos_test_ratio[0] <= 1.0f) && (_vel_pos_test_ratio[1] <= 1.0f) && (_vel_pos_test_ratio[2] <= 1.0f);
    innov_check_pass_map[2] = innov_check_pass_map[1] = innov_check_pass_map[0] = vel_check_pass;
    bool using_synthetic_measurements = !_control_status.flags.gps && !_control_status.flags.opt_flow;
    bool pos_check_pass = ((_vel_pos_test_ratio[3] <= 1.0f) && (_vel_pos_test_ratio[4] <= 1.0f)) || using_synthetic_measurements;
    innov_check_pass_map[4] = innov_check_pass_map[3] = pos_check_pass;
    innov_check_pass_map[5] = (_vel_pos_test_ratio[5] <= 1.0f);

    // record the successful velocity fusion time
    if (vel_check_pass && _fuse_hor_vel) {
        _time_last_vel_fuse = _time_last_imu;
    }

    // record the successful position fusion time
    if (pos_check_pass && _fuse_pos) {
        _time_last_pos_fuse = _time_last_imu;
    }

    // record the successful height fusion time
    if (innov_check_pass_map[5] && _fuse_height) {
        _time_last_hgt_fuse = _time_last_imu;
    }

    for (unsigned obs_index = 0; obs_index < 6; obs_index++) {
        // skip fusion if not requested or checks have failed
        if (!fuse_map[obs_index] || !innov_check_pass_map[obs_index]) {
			continue;
		}

		unsigned state_index = obs_index + 3;	// we start with vx and this is the 4. state

        // calculate kalman gain K = PHS, where S = 1/innovation variance
		for (int row = 0; row < 24; row++) {
            Kfusion[row] = P[row][state_index] / _vel_pos_innov_var[obs_index];
		}

		// by definition the angle error state is zero at the fusion time
		_state.ang_error.setZero();

		// fuse the observation
		fuse(Kfusion, _vel_pos_innov[obs_index]);

		// correct the nominal quaternion
		Quaternion dq;
		dq.from_axis_angle(_state.ang_error);
		_state.quat_nominal = dq * _state.quat_nominal;
		_state.quat_nominal.normalize();

		// update covarinace matrix via Pnew = (I - KH)P
		float KHP[_k_num_states][_k_num_states] = {};

		for (unsigned row = 0; row < _k_num_states; row++) {
			for (unsigned column = 0; column < _k_num_states; column++) {
				KHP[row][column] = Kfusion[row] * P[state_index][column];
			}
		}

		for (unsigned row = 0; row < _k_num_states; row++) {
			for (unsigned column = 0; column < _k_num_states; column++) {
				P[row][column] = P[row][column] - KHP[row][column];
			}
		}

		makeSymmetrical();
		limitCov();
	}

}

void Ekf::fuse(float *K, float innovation)
{
	for (unsigned i = 0; i < 3; i++) {
		_state.ang_error(i) = _state.ang_error(i) - K[i] * innovation;
	}

	for (unsigned i = 0; i < 3; i++) {
		_state.vel(i) = _state.vel(i) - K[i + 3] * innovation;
	}

	for (unsigned i = 0; i < 3; i++) {
		_state.pos(i) = _state.pos(i) - K[i + 6] * innovation;
	}

	for (unsigned i = 0; i < 3; i++) {
		_state.gyro_bias(i) = _state.gyro_bias(i) - K[i + 9] * innovation;
	}

	for (unsigned i = 0; i < 3; i++) {
		_state.gyro_scale(i) = _state.gyro_scale(i) - K[i + 12] * innovation;
	}

	_state.accel_z_bias -= K[15] * innovation;

	for (unsigned i = 0; i < 3; i++) {
		_state.mag_I(i) = _state.mag_I(i) - K[i + 16] * innovation;
	}

	for (unsigned i = 0; i < 3; i++) {
		_state.mag_B(i) = _state.mag_B(i) - K[i + 19] * innovation;
	}

	for (unsigned i = 0; i < 2; i++) {
		_state.wind_vel(i) = _state.wind_vel(i) - K[i + 22] * innovation;
	}
}