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/**
 * @file gravity_fusion.cpp
 * Fuse observations from the gravity vector to constrain roll
 * and pitch (a la complementary filter).
 *
 * @author Daniel M. Sahu <danielmohansahu@gmail.com>
 */

#include "ekf.h"
#include <ekf_derivation/generated/compute_gravity_xyz_innov_var_and_hx.h>
#include <ekf_derivation/generated/compute_gravity_y_innov_var_and_h.h>
#include <ekf_derivation/generated/compute_gravity_z_innov_var_and_h.h>

#include <mathlib/mathlib.h>

void Ekf::controlGravityFusion(const imuSample &imu)
{
	// get raw accelerometer reading at delayed horizon and expected measurement noise (gaussian)
	const Vector3f measurement = Vector3f(imu.delta_vel / imu.delta_vel_dt - _state.accel_bias).unit();
	const float measurement_var = math::max(sq(_params.gravity_noise), sq(0.01f));

	const float upper_accel_limit = CONSTANTS_ONE_G * 1.1f;
	const float lower_accel_limit = CONSTANTS_ONE_G * 0.9f;
	const bool accel_lpf_norm_good = (_accel_magnitude_filt > lower_accel_limit) && (_accel_magnitude_filt < upper_accel_limit);

	// fuse gravity observation if our overall acceleration isn't too big
	_control_status.flags.gravity_vector = (_params.imu_ctrl & static_cast<int32_t>(ImuCtrl::GravityVector))
					       && (accel_lpf_norm_good || _control_status.flags.vehicle_at_rest)
					       && !isHorizontalAidingActive();

	// calculate kalman gains and innovation variances
	Vector3f innovation = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f)) - measurement;
	Vector3f innovation_variance;
	const auto state_vector = _state.vector();
	VectorState H;
	sym::ComputeGravityXyzInnovVarAndHx(state_vector, P, measurement_var, &innovation_variance, &H);

	// fill estimator aid source status
	resetEstimatorAidStatus(_aid_src_gravity);
	_aid_src_gravity.timestamp_sample = imu.time_us;
	measurement.copyTo(_aid_src_gravity.observation);

	for (auto &var : _aid_src_gravity.observation_variance) {
		var = measurement_var;
	}

	innovation.copyTo(_aid_src_gravity.innovation);
	innovation_variance.copyTo(_aid_src_gravity.innovation_variance);

	float innovation_gate = 0.25f;
	setEstimatorAidStatusTestRatio(_aid_src_gravity, innovation_gate);

	bool fused[3] {false, false, false};

	// update the states and covariance using sequential fusion
	for (uint8_t index = 0; index <= 2; index++) {
		// Calculate Kalman gains and observation jacobians
		if (index == 0) {
			// everything was already computed above

		} else if (index == 1) {
			// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
			sym::ComputeGravityYInnovVarAndH(state_vector, P, measurement_var, &_aid_src_gravity.innovation_variance[index], &H);

			// recalculate innovation using the updated state
			_aid_src_gravity.innovation[index] = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f))(index) - measurement(index);

		} else if (index == 2) {
			// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
			sym::ComputeGravityZInnovVarAndH(state_vector, P, measurement_var, &_aid_src_gravity.innovation_variance[index], &H);

			// recalculate innovation using the updated state
			_aid_src_gravity.innovation[index] = _state.quat_nominal.rotateVectorInverse(Vector3f(0.f, 0.f, -1.f))(index) - measurement(index);
		}

		VectorState K = P * H / _aid_src_gravity.innovation_variance[index];

		const bool accel_clipping = imu.delta_vel_clipping[0] || imu.delta_vel_clipping[1] || imu.delta_vel_clipping[2];

		if (_control_status.flags.gravity_vector && !_aid_src_gravity.innovation_rejected && !accel_clipping) {
			fused[index] = measurementUpdate(K, H, _aid_src_gravity.observation_variance[index], _aid_src_gravity.innovation[index]);
		}
	}

	if (fused[0] && fused[1] && fused[2]) {
		_aid_src_gravity.fused = true;
		_aid_src_gravity.time_last_fuse = imu.time_us;
	}
}