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- refactor all EKF backend output predictor pieces into new OutputPredictor class - output states are now calculated immediately with new high rate IMU rather than after EKF update - IMU delayed sample is passed as around as control data to avoid storing an extra copy and make the requirement clear
172 lines
5.7 KiB
C++
172 lines
5.7 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2015 Estimation and Control Library (ECL). All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* the documentation and/or other materials provided with the
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* 3. Neither the name ECL nor the names of its contributors may be
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* without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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*
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****************************************************************************/
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/**
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* @file airspeed_fusion.cpp
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* airspeed fusion methods.
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* equations generated using EKF/python/ekf_derivation/main.py
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*
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* @author Carl Olsson <carlolsson.co@gmail.com>
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* @author Roman Bast <bapstroman@gmail.com>
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* @author Paul Riseborough <p_riseborough@live.com.au>
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*
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*/
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#include "ekf.h"
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#include "python/ekf_derivation/generated/compute_airspeed_h_and_k.h"
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#include "python/ekf_derivation/generated/compute_airspeed_innov_and_innov_var.h"
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#include <mathlib/mathlib.h>
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void Ekf::updateAirspeed(const airspeedSample &airspeed_sample, estimator_aid_source1d_s &airspeed) const
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{
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// reset flags
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resetEstimatorAidStatus(airspeed);
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// Variance for true airspeed measurement - (m/sec)^2
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const float R = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) *
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math::constrain(airspeed_sample.eas2tas, 0.9f, 10.0f));
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float innov = 0.f;
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float innov_var = 0.f;
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sym::ComputeAirspeedInnovAndInnovVar(getStateAtFusionHorizonAsVector(), P, airspeed_sample.true_airspeed, R, FLT_EPSILON, &innov, &innov_var);
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airspeed.observation = airspeed_sample.true_airspeed;
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airspeed.observation_variance = R;
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airspeed.innovation = innov;
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airspeed.innovation_variance = innov_var;
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airspeed.fusion_enabled = _control_status.flags.fuse_aspd;
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airspeed.timestamp_sample = airspeed_sample.time_us;
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const float innov_gate = fmaxf(_params.tas_innov_gate, 1.f);
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setEstimatorAidStatusTestRatio(airspeed, innov_gate);
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}
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void Ekf::fuseAirspeed(estimator_aid_source1d_s &airspeed)
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{
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if (airspeed.innovation_rejected) {
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return;
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}
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// determine if we need the airspeed fusion to correct states other than wind
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const bool update_wind_only = !_control_status.flags.wind_dead_reckoning;
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const float innov_var = airspeed.innovation_variance;
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if (innov_var < airspeed.observation_variance || innov_var < FLT_EPSILON) {
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// Reset the estimator covariance matrix
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// if we are getting aiding from other sources, warn and reset the wind states and covariances only
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const char *action_string = nullptr;
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if (update_wind_only) {
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resetWindUsingAirspeed();
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action_string = "wind";
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} else {
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initialiseCovariance();
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_state.wind_vel.setZero();
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action_string = "full";
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}
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ECL_ERR("airspeed badly conditioned - %s covariance reset", action_string);
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_fault_status.flags.bad_airspeed = true;
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return;
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}
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_fault_status.flags.bad_airspeed = false;
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Vector24f H; // Observation jacobian
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Vector24f K; // Kalman gain vector
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sym::ComputeAirspeedHAndK(getStateAtFusionHorizonAsVector(), P, innov_var, FLT_EPSILON, &H, &K);
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if (update_wind_only) {
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for (unsigned row = 0; row <= 21; row++) {
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K(row) = 0.f;
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}
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}
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const bool is_fused = measurementUpdate(K, airspeed.innovation_variance, airspeed.innovation);
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airspeed.fused = is_fused;
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_fault_status.flags.bad_airspeed = !is_fused;
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if (is_fused) {
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airspeed.time_last_fuse = _time_delayed_us;
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}
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}
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float Ekf::getTrueAirspeed() const
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{
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return (_state.vel - Vector3f(_state.wind_vel(0), _state.wind_vel(1), 0.f)).norm();
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}
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void Ekf::resetWind()
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{
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if (_control_status.flags.fuse_aspd) {
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resetWindUsingAirspeed();
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} else {
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resetWindToZero();
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}
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}
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/*
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* Reset the wind states using the current airspeed measurement, ground relative nav velocity, yaw angle and assumption of zero sideslip
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*/
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void Ekf::resetWindUsingAirspeed()
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{
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const float euler_yaw = getEulerYaw(_R_to_earth);
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// estimate wind using zero sideslip assumption and airspeed measurement if airspeed available
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_state.wind_vel(0) = _state.vel(0) - _airspeed_sample_delayed.true_airspeed * cosf(euler_yaw);
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_state.wind_vel(1) = _state.vel(1) - _airspeed_sample_delayed.true_airspeed * sinf(euler_yaw);
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resetWindCovarianceUsingAirspeed();
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_aid_src_airspeed.time_last_fuse = _time_delayed_us;
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}
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void Ekf::resetWindToZero()
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{
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// If we don't have an airspeed measurement, then assume the wind is zero
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_state.wind_vel.setZero();
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// start with a small initial uncertainty to improve the initial estimate
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P.uncorrelateCovarianceSetVariance<2>(22, _params.initial_wind_uncertainty);
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}
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