/**************************************************************************** * * Copyright (c) 2015 Estimation and Control Library (ECL). All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * 3. Neither the name ECL nor the names of its contributors may be * used to endorse or promote products derived from this software * without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * ****************************************************************************/ /** * @file airspeed_fusion.cpp * airspeed fusion methods. * equations generated using EKF/python/ekf_derivation/main.py * * @author Carl Olsson * @author Roman Bast * @author Paul Riseborough * */ #include "ekf.h" #include void Ekf::updateAirspeed(const airspeedSample &airspeed_sample, estimator_aid_source_1d_s &airspeed) const { // reset flags resetEstimatorAidStatusFlags(airspeed); const float vn = _state.vel(0); // Velocity in north direction const float ve = _state.vel(1); // Velocity in east direction const float vd = _state.vel(2); // Velocity in downwards direction const float vwn = _state.wind_vel(0); // Wind speed in north direction const float vwe = _state.wind_vel(1); // Wind speed in east direction // Variance for true airspeed measurement - (m/sec)^2 const float R_TAS = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) * math::constrain(airspeed_sample.eas2tas, 0.9f, 10.0f)); // Intermediate variables const float IV0 = ve - vwe; const float IV1 = vn - vwn; const float IV2 = (IV0)*(IV0) + (IV1)*(IV1) + (vd)*(vd); const float predicted_airspeed = sqrtf(IV2); if (fabsf(predicted_airspeed) < FLT_EPSILON) { return; } const float IV3 = 1.0F/(IV2); const float IV4 = IV0*P(5,23); const float IV5 = IV0*IV3; const float IV6 = IV1*P(4,22); const float IV7 = IV1*IV3; const float innov_var = IV3*vd*(IV0*P(5,6) - IV0*P(6,23) + IV1*P(4,6) - IV1*P(6,22) + P(6,6)*vd) - IV5*(-IV0*P(23,23) - IV1*P(22,23) + IV1*P(4,23) + IV4 + P(6,23)*vd) + IV5*(IV0*P(5,5) + IV1*P(4,5) - IV1*P(5,22) - IV4 + P(5,6)*vd) - IV7*(-IV0*P(22,23) + IV0*P(5,22) - IV1*P(22,22) + IV6 + P(6,22)*vd) + IV7*(-IV0*P(4,23) + IV0*P(4,5) + IV1*P(4,4) - IV6 + P(4,6)*vd) + R_TAS; airspeed.observation = airspeed_sample.true_airspeed; airspeed.observation_variance = R_TAS; airspeed.innovation = predicted_airspeed - airspeed.observation; airspeed.innovation_variance = innov_var; airspeed.fusion_enabled = _control_status.flags.fuse_aspd; airspeed.timestamp_sample = airspeed_sample.time_us; const float innov_gate = fmaxf(_params.tas_innov_gate, 1.f); setEstimatorAidStatusTestRatio(airspeed, innov_gate); } void Ekf::fuseAirspeed(estimator_aid_source_1d_s &airspeed) { if (airspeed.innovation_rejected) { return; } const float vn = _state.vel(0); // Velocity in north direction const float ve = _state.vel(1); // Velocity in east direction const float vd = _state.vel(2); // Velocity in downwards direction const float vwn = _state.wind_vel(0); // Wind speed in north direction const float vwe = _state.wind_vel(1); // Wind speed in east direction // determine if we need the airspeed fusion to correct states other than wind const bool update_wind_only = !_control_status.flags.wind_dead_reckoning; // Intermediate variables const float HK0 = vn - vwn; const float HK1 = ve - vwe; const float HK2 = sqrtf((HK0)*(HK0) + (HK1)*(HK1) + (vd)*(vd)); const float predicted_airspeed = HK2; if (predicted_airspeed < 1.0f) { // calculation can be badly conditioned for very low airspeed values so don't fuse this time return; } const float HK3 = 1.0F/(HK2); const float HK4 = HK0*HK3; const float HK5 = HK1*HK3; const float HK6 = HK3*vd; const float HK7 = -HK0*HK3; const float HK8 = -HK1*HK3; const float innov_var = airspeed.innovation_variance; if (innov_var < airspeed.observation_variance || innov_var < FLT_EPSILON) { // Reset the estimator covariance matrix // if we are getting aiding from other sources, warn and reset the wind states and covariances only const char *action_string = nullptr; if (update_wind_only) { resetWindUsingAirspeed(); action_string = "wind"; } else { initialiseCovariance(); _state.wind_vel.setZero(); action_string = "full"; } ECL_ERR("airspeed badly conditioned - %s covariance reset", action_string); _fault_status.flags.bad_airspeed = true; return; } const float HK9 = 1.0F/(innov_var); _fault_status.flags.bad_airspeed = false; // Observation Jacobians SparseVector24f<4,5,6,22,23> Hfusion; Hfusion.at<4>() = HK4; Hfusion.at<5>() = HK5; Hfusion.at<6>() = HK6; Hfusion.at<22>() = HK7; Hfusion.at<23>() = HK8; Vector24f Kfusion; // Kalman gain vector if (!update_wind_only) { // we have no other source of aiding, so use airspeed measurements to correct states for (unsigned row = 0; row <= 21; row++) { Kfusion(row) = HK9*(HK4*P(row,4) + HK5*P(row,5) + HK6*P(row,6) + HK7*P(row,22) + HK8*P(row,23)); } } Kfusion(22) = HK9*(HK4*P(4,22) + HK5*P(5,22) + HK6*P(6,22) + HK7*P(22,22) + HK8*P(22,23)); Kfusion(23) = HK9*(HK4*P(4,23) + HK5*P(5,23) + HK6*P(6,23) + HK7*P(22,23) + HK8*P(23,23)); const bool is_fused = measurementUpdate(Kfusion, Hfusion, airspeed.innovation); airspeed.fused = is_fused; _fault_status.flags.bad_airspeed = !is_fused; if (is_fused) { airspeed.time_last_fuse = _imu_sample_delayed.time_us; } } float Ekf::getTrueAirspeed() const { return (_state.vel - Vector3f(_state.wind_vel(0), _state.wind_vel(1), 0.f)).norm(); } void Ekf::resetWind() { if (_control_status.flags.fuse_aspd) { resetWindUsingAirspeed(); } else { resetWindToZero(); } } /* * Reset the wind states using the current airspeed measurement, ground relative nav velocity, yaw angle and assumption of zero sideslip */ void Ekf::resetWindUsingAirspeed() { const float euler_yaw = getEulerYaw(_R_to_earth); // estimate wind using zero sideslip assumption and airspeed measurement if airspeed available _state.wind_vel(0) = _state.vel(0) - _airspeed_sample_delayed.true_airspeed * cosf(euler_yaw); _state.wind_vel(1) = _state.vel(1) - _airspeed_sample_delayed.true_airspeed * sinf(euler_yaw); resetWindCovarianceUsingAirspeed(); _aid_src_airspeed.time_last_fuse = _imu_sample_delayed.time_us; } void Ekf::resetWindToZero() { // If we don't have an airspeed measurement, then assume the wind is zero _state.wind_vel.setZero(); // start with a small initial uncertainty to improve the initial estimate P.uncorrelateCovarianceSetVariance<2>(22, _params.initial_wind_uncertainty); }