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/**
 * @file height_fusion.cpp
 * Function for fusing height (range, baro, GNSS alt, ...) measurements
 */

#include "ekf.h"

void Ekf::updateBaroHgt(const baroSample &baro_sample, estimator_aid_source_1d_s &baro_hgt)
{
	// reset flags
	resetEstimatorAidStatus(baro_hgt);

	// innovation gate size
	const float innov_gate = fmaxf(_params.baro_innov_gate, 1.f);

	// measurement variance - user parameter defined
	const float measurement_var = sq(fmaxf(_params.baro_noise, 0.01f));
	const float measurement = baro_sample.hgt;

	// vertical position innovation - baro measurement has opposite sign to earth z axis
	baro_hgt.observation = -(measurement - _baro_b_est.getBias());
	baro_hgt.observation_variance = measurement_var + _baro_b_est.getBiasVar();

	baro_hgt.innovation = _state.pos(2) - baro_hgt.observation;
	baro_hgt.innovation_variance = P(9, 9) + baro_hgt.observation_variance;

	// Compensate for positive static pressure transients (negative vertical position innovations)
	// caused by rotor wash ground interaction by applying a temporary deadzone to baro innovations.
	if (_control_status.flags.gnd_effect && (_params.gnd_effect_deadzone > 0.f)) {

		const float deadzone_start = 0.0f;
		const float deadzone_end = deadzone_start + _params.gnd_effect_deadzone;

		if (baro_hgt.innovation < -deadzone_start) {
			if (baro_hgt.innovation <= -deadzone_end) {
				baro_hgt.innovation += deadzone_end;

			} else {
				baro_hgt.innovation = -deadzone_start;
			}
		}
	}

	setEstimatorAidStatusTestRatio(baro_hgt, innov_gate);

	// special case if there is bad vertical acceleration data, then don't reject measurement,
	// but limit innovation to prevent spikes that could destabilise the filter
	if (_fault_status.flags.bad_acc_vertical && baro_hgt.innovation_rejected) {
		const float innov_limit = innov_gate * sqrtf(baro_hgt.innovation_variance);
		baro_hgt.innovation = math::constrain(baro_hgt.innovation, -innov_limit, innov_limit);
		baro_hgt.innovation_rejected = false;
	}

	baro_hgt.fusion_enabled = _control_status.flags.baro_hgt;

	baro_hgt.timestamp_sample = baro_sample.time_us;

	// update the bias estimator before updating the main filter but after
	// using its current state to compute the vertical position innovation
	_baro_b_est.setMaxStateNoise(_params.baro_noise);
	_baro_b_est.setProcessNoiseSpectralDensity(_params.baro_bias_nsd);
	_baro_b_est.fuseBias(measurement - (-_state.pos(2)) , measurement_var + P(9, 9));
}

void Ekf::fuseBaroHgt(estimator_aid_source_1d_s &baro_hgt)
{
	if (baro_hgt.fusion_enabled
	    && !baro_hgt.innovation_rejected
	    && fuseVelPosHeight(baro_hgt.innovation, baro_hgt.innovation_variance, 5)) {

		baro_hgt.fused = true;
		baro_hgt.time_last_fuse = _imu_sample_delayed.time_us;
	}
}

void Ekf::updateRngHgt(estimator_aid_source_1d_s &rng_hgt)
{
	// reset flags
	resetEstimatorAidStatus(rng_hgt);

	// measurement variance - user parameter defined
	const float measurement_var = fmaxf(sq(_params.range_noise) + sq(_params.range_noise_scaler * _range_sensor.getDistBottom()), 0.01f);
	const float measurement = math::max(_range_sensor.getDistBottom(), _params.rng_gnd_clearance);

	// innovation gate size
	const float innov_gate = fmaxf(_params.range_innov_gate, 1.f);

	// vertical position innovation, use range finder with tilt correction
	rng_hgt.observation = -(measurement - _rng_hgt_b_est.getBias());
	rng_hgt.observation_variance = measurement_var + _rng_hgt_b_est.getBiasVar();

	rng_hgt.innovation = _state.pos(2) - rng_hgt.observation;
	rng_hgt.innovation_variance = P(9, 9) + rng_hgt.observation_variance;

	setEstimatorAidStatusTestRatio(rng_hgt, innov_gate);

	// special case if there is bad vertical acceleration data, then don't reject measurement,
	// but limit innovation to prevent spikes that could destabilise the filter
	if (_fault_status.flags.bad_acc_vertical && rng_hgt.innovation_rejected) {
		const float innov_limit = innov_gate * sqrtf(rng_hgt.innovation_variance);
		rng_hgt.innovation = math::constrain(rng_hgt.innovation, -innov_limit, innov_limit);
		rng_hgt.innovation_rejected = false;
	}

	rng_hgt.fusion_enabled = _control_status.flags.rng_hgt;

	rng_hgt.timestamp_sample = _range_sensor.getSampleAddress()->time_us;

	// update the bias estimator before updating the main filter but after
	// using its current state to compute the vertical position innovation
	const float rng_noise = sqrtf(measurement_var);
	_rng_hgt_b_est.setMaxStateNoise(rng_noise);
	_rng_hgt_b_est.setProcessNoiseSpectralDensity(_params.rng_hgt_bias_nsd);

	_rng_hgt_b_est.fuseBias(measurement - (-_state.pos(2)) , measurement_var + P(9, 9));
}

void Ekf::fuseRngHgt(estimator_aid_source_1d_s &rng_hgt)
{
	if (rng_hgt.fusion_enabled
	    && !rng_hgt.innovation_rejected
	    && fuseVelPosHeight(rng_hgt.innovation, rng_hgt.innovation_variance, 5)) {

		rng_hgt.fused = true;
		rng_hgt.time_last_fuse = _imu_sample_delayed.time_us;
	}
}

void Ekf::fuseEvHgt()
{
	const float measurement = _ev_sample_delayed.pos(2);
	const float measurement_var = fmaxf(_ev_sample_delayed.posVar(2), sq(0.01f));

	const float bias = _ev_hgt_b_est.getBias();
	const float bias_var = _ev_hgt_b_est.getBiasVar();

	_ev_hgt_b_est.setMaxStateNoise(sqrtf(measurement_var));
	_ev_hgt_b_est.setProcessNoiseSpectralDensity(_params.ev_hgt_bias_nsd);
	_ev_hgt_b_est.fuseBias(measurement - _state.pos(2), measurement_var + P(9, 9));

	// calculate the innovation assuming the external vision observation is in local NED frame
	const float obs = measurement - bias;
	const float obs_var = measurement_var + bias_var;

	// innovation gate size
	float innov_gate = fmaxf(_params.ev_pos_innov_gate, 1.f);

	updateVerticalPositionAidSrcStatus(_ev_sample_delayed.time_us, obs, obs_var, innov_gate, _aid_src_ev_hgt);

	_aid_src_ev_hgt.fusion_enabled = _control_status.flags.ev_hgt;

	fuseVerticalPosition(_aid_src_ev_hgt);
}