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>
 * @author Siddharth Bharat Purohit <siddharthbharatpurohit@gmail.com>
 * @author Paul Riseborough <p_riseborough@live.com.au>
 *
 */

#include "ekf.h"
#include <ecl.h>
#include <mathlib/mathlib.h>


bool Ekf::fuseHorizontalVelocity(const Vector3f &innov, const Vector2f &innov_gate,
				 const Vector3f &obs_var, Vector3f &innov_var, Vector2f &test_ratio)
{
	innov_var(0) = P(4,4) + obs_var(0);
	innov_var(1) = P(5,5) + obs_var(1);
	test_ratio(0) = fmaxf( sq(innov(0)) / (sq(innov_gate(0)) * innov_var(0)),
			       sq(innov(1)) / (sq(innov_gate(0)) * innov_var(1)));

	bool innov_check_pass = (test_ratio(0) <= 1.0f);
	if (innov_check_pass)
	{
		_time_last_hor_vel_fuse = _time_last_imu;
		_innov_check_fail_status.flags.reject_hor_vel = false;

		fuseVelPosHeight(innov(0),innov_var(0),0);
		fuseVelPosHeight(innov(1),innov_var(1),1);

		return true;
	}else{
		_innov_check_fail_status.flags.reject_hor_vel = true;
		return false;
	}
}

bool Ekf::fuseVerticalVelocity(const Vector3f &innov, const Vector2f &innov_gate,
				 const Vector3f &obs_var, Vector3f &innov_var, Vector2f &test_ratio)
{
	innov_var(2) = P(6,6) + obs_var(2);
	test_ratio(1) = sq(innov(2)) / (sq(innov_gate(1)) * innov_var(2));

	bool innov_check_pass = (test_ratio(1) <= 1.0f);
	if (innov_check_pass) {
		_time_last_ver_vel_fuse = _time_last_imu;
		_innov_check_fail_status.flags.reject_ver_vel = false;

		fuseVelPosHeight(innov(2),innov_var(2),2);

		return true;
	}else{
		_innov_check_fail_status.flags.reject_ver_vel = true;
		return false;
	}
}

bool Ekf::fuseHorizontalPosition(const Vector3f &innov, const Vector2f &innov_gate,
				 const Vector3f &obs_var, Vector3f &innov_var, Vector2f &test_ratio)
{
		innov_var(0) = P(7,7) + obs_var(0);
		innov_var(1) = P(8,8) + obs_var(1);
		test_ratio(0) = fmaxf( sq(innov(0)) / (sq(innov_gate(0)) * innov_var(0)),
				       sq(innov(1)) / (sq(innov_gate(0)) * innov_var(1)));

		bool innov_check_pass = (test_ratio(0) <= 1.0f) || !_control_status.flags.tilt_align;
		if (innov_check_pass) {
			if (!_fuse_hpos_as_odom) {
				_time_last_hor_pos_fuse = _time_last_imu;

			} else {
				_time_last_delpos_fuse = _time_last_imu;
			}
			_innov_check_fail_status.flags.reject_hor_pos = false;

			fuseVelPosHeight(innov(0),innov_var(0),3);
			fuseVelPosHeight(innov(1),innov_var(1),4);

			return true;
		}else{
			_innov_check_fail_status.flags.reject_hor_pos = true;
			return false;
		}
}

bool Ekf::fuseVerticalPosition(const Vector3f &innov, const Vector2f &innov_gate,
				 const Vector3f &obs_var, Vector3f &innov_var, Vector2f &test_ratio)
{
	innov_var(2) = P(9,9) + obs_var(2);
	test_ratio(1) = sq(innov(2)) / (sq(innov_gate(1)) * innov_var(2));

	bool innov_check_pass = (test_ratio(1) <= 1.0f) || !_control_status.flags.tilt_align;
	if (innov_check_pass) {
		_time_last_hgt_fuse = _time_last_imu;
		_innov_check_fail_status.flags.reject_ver_pos = false;

		fuseVelPosHeight(innov(2),innov_var(2),5);

		return true;
	}else{
		_innov_check_fail_status.flags.reject_ver_pos = true;
		return false;
	}
}

// Helper function that fuses a single velocity or position measurement
void Ekf::fuseVelPosHeight(const float innov, const float innov_var, const int obs_index)
{
	float Kfusion[24] = {}; // Kalman gain vector for any single observation - sequential fusion is used.
	unsigned state_index = obs_index + 4;	// 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 < _k_num_states; row++) {
		Kfusion[row] = P(row,state_index) / innov_var;
	}

	matrix::SquareMatrix<float, _k_num_states> KHP;

	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);
		}
	}

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

	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);

			healthy = false;

			setVelPosFaultStatus(obs_index, true);

		} else {
			setVelPosFaultStatus(obs_index, false);
		}
	}


	// only apply covariance and state corrections if healthy
	if (healthy) {
		// apply the covariance corrections
		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);
			}
		}

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

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

	}
}

void Ekf::setVelPosFaultStatus(const int index, const bool status)
{
	if (index == 0) {
		_fault_status.flags.bad_vel_N = status;

	} else if (index == 1) {
		_fault_status.flags.bad_vel_E = status;

	} else if (index == 2) {
		_fault_status.flags.bad_vel_D = status;

	} else if (index == 3) {
		_fault_status.flags.bad_pos_N = status;

	} else if (index == 4) {
		_fault_status.flags.bad_pos_E = status;

	} else if (index == 5) {
		_fault_status.flags.bad_pos_D = status;
	}
}