px4_autopilot/EKF/vel_pos_fusion.cpp

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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] = {};
	float innovations[6] = {};
	float R[6] = {};
	float Kfusion[24] = {};

	// calculate innovations
	if (_fuse_hor_vel) {
		fuse_map[0] = fuse_map[1] = true;
		innovations[0] = _state.vel(0) - _gps_sample_delayed.vel(0);
		innovations[1] = _state.vel(1) - _gps_sample_delayed.vel(1);
		R[0] = _params.gps_vel_noise;
		R[1] = _params.gps_vel_noise;
	}

	if (_fuse_vert_vel) {
		fuse_map[2] = true;
		innovations[2] = _state.vel(2) - _gps_sample_delayed.vel(2);
		R[2] = _params.gps_vel_noise;
	}

	if (_fuse_pos) {
		fuse_map[3] = fuse_map[4] = true;
		innovations[3] = _state.pos(0) - _gps_sample_delayed.pos(0);
		innovations[4] = _state.pos(1) - _gps_sample_delayed.pos(1);
		R[3] = _params.gps_pos_noise;
		R[4] = _params.gps_pos_noise;

	}

	if (_fuse_height) {
		fuse_map[5] = true;
		innovations[5] = _state.pos(2) - (-_baro_sample_delayed.hgt);		// baro measurement has inversed z axis
		R[5] = _params.baro_noise;
	}

	// XXX Do checks here

	for (unsigned obs_index = 0; obs_index < 6; obs_index++) {
		if (!fuse_map[obs_index]) {
			continue;
		}

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

		// compute the innovation variance SK = HPH + R
		float S = P[state_index][state_index] + R[obs_index];
		S = 1.0f / S;

		// calculate kalman gain K = PHS
		for (int row = 0; row < 24; row++) {
			Kfusion[row] = P[row][state_index] * S;
		}

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

		// fuse the observation
		fuse(Kfusion, innovations[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;
	}
}