2016-03-11 07:42:16 -04:00
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/****************************************************************************
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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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*
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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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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name ECL nor the names of its contributors may be
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* used to endorse or promote products derived from this software
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
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* POSSIBILITY OF SUCH DAMAGE.
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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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*
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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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2016-06-07 07:32:55 -03:00
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#include "../ecl.h"
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2016-03-11 07:42:16 -04:00
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#include "ekf.h"
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#include "mathlib.h"
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void Ekf::fuseAirspeed()
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{
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// Initialize variables
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float vn; // Velocity in north direction
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float ve; // Velocity in east direction
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float vd; // Velocity in downwards direction
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float vwn; // Wind speed in north direction
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float vwe; // Wind speed in east direction
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float v_tas_pred; // Predicted measurement
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2016-04-05 10:14:04 -03:00
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float R_TAS = sq(math::constrain(_params.eas_noise, 0.5f, 5.0f) * math::constrain(_airspeed_sample_delayed.eas2tas, 0.9f,
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10.0f)); // Variance for true airspeed measurement - (m/sec)^2
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2016-03-11 07:42:16 -04:00
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float SH_TAS[3] = {}; // Varialbe used to optimise calculations of measurement jacobian
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float H_TAS[24] = {}; // Observation Jacobian
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float SK_TAS[2] = {}; // Varialbe used to optimise calculations of the Kalman gain vector
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float Kfusion[24] = {}; // Kalman gain vector
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// Copy required states to local variable names
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vn = _state.vel(0);
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ve = _state.vel(1);
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vd = _state.vel(2);
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vwn = _state.wind_vel(0);
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vwe = _state.wind_vel(1);
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// Calculate the predicted airspeed
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v_tas_pred = sqrtf((ve - vwe) * (ve - vwe) + (vn - vwn) * (vn - vwn) + vd * vd);
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// Perform fusion of True Airspeed measurement
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2016-04-03 15:08:51 -03:00
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if (v_tas_pred > 1.0f) {
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2016-03-11 07:42:16 -04:00
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// Calculate the observation jacobian
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// intermediate variable from algebraic optimisation
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2016-04-27 23:05:54 -03:00
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SH_TAS[0] = 1.0f/v_tas_pred;
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SH_TAS[1] = (SH_TAS[0]*(2.0f*ve - 2.0f*vwe))*0.5f;
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SH_TAS[2] = (SH_TAS[0]*(2.0f*vn - 2.0f*vwn))*0.5f;
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2016-03-11 07:42:16 -04:00
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for (uint8_t i = 0; i < _k_num_states; i++) { H_TAS[i] = 0.0f; }
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2016-04-27 23:05:54 -03:00
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H_TAS[4] = SH_TAS[2];
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H_TAS[5] = SH_TAS[1];
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H_TAS[6] = vd*SH_TAS[0];
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2016-03-11 07:42:16 -04:00
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H_TAS[22] = -SH_TAS[2];
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H_TAS[23] = -SH_TAS[1];
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2016-04-05 10:14:04 -03:00
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2016-03-11 07:42:16 -04:00
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// We don't want to update the innovation variance if the calculation is ill conditioned
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2016-04-27 23:05:54 -03:00
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float _airspeed_innov_var_temp = (R_TAS + SH_TAS[2]*(P[4][4]*SH_TAS[2] + P[5][4]*SH_TAS[1] - P[22][4]*SH_TAS[2] - P[23][4]*SH_TAS[1] + P[6][4]*vd*SH_TAS[0]) + SH_TAS[1]*(P[4][5]*SH_TAS[2] + P[5][5]*SH_TAS[1] - P[22][5]*SH_TAS[2] - P[23][5]*SH_TAS[1] + P[6][5]*vd*SH_TAS[0]) - SH_TAS[2]*(P[4][22]*SH_TAS[2] + P[5][22]*SH_TAS[1] - P[22][22]*SH_TAS[2] - P[23][22]*SH_TAS[1] + P[6][22]*vd*SH_TAS[0]) - SH_TAS[1]*(P[4][23]*SH_TAS[2] + P[5][23]*SH_TAS[1] - P[22][23]*SH_TAS[2] - P[23][23]*SH_TAS[1] + P[6][23]*vd*SH_TAS[0]) + vd*SH_TAS[0]*(P[4][6]*SH_TAS[2] + P[5][6]*SH_TAS[1] - P[22][6]*SH_TAS[2] - P[23][6]*SH_TAS[1] + P[6][6]*vd*SH_TAS[0]));
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2016-04-05 10:14:04 -03:00
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2016-03-11 07:42:16 -04:00
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if (_airspeed_innov_var_temp >= R_TAS) { // Check for badly conditioned calculation
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2016-04-05 10:14:04 -03:00
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SK_TAS[0] = 1.0f / _airspeed_innov_var_temp;
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2016-05-12 05:06:04 -03:00
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_fault_status.flags.bad_airspeed = false;
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2016-04-05 10:14:04 -03:00
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2016-06-07 07:32:55 -03:00
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} else { // Reset the estimator covarinace matrix
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2016-05-12 05:06:04 -03:00
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_fault_status.flags.bad_airspeed = true;
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2016-04-05 10:14:04 -03:00
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initialiseCovariance();
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2016-06-07 07:32:55 -03:00
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ECL_ERR("EKF airspeed fusion numerical error - covariance reset");
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2016-04-05 10:14:04 -03:00
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return;
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}
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2016-03-11 07:42:16 -04:00
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SK_TAS[1] = SH_TAS[1];
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2016-10-24 18:02:03 -03:00
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if (((_time_last_imu - _time_last_gps) < 1e6) || ((_time_last_imu - _time_last_ext_vision) < 1e6) || ((_time_last_imu - _time_last_optflow) < 1e6)) {
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// If we are getting aiding from other sources, then don't allow the airspeed measurements to affect the non-windspeed states
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for (unsigned row = 0; row <= 21; row++) {
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Kfusion[row] = 0.0f;
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}
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} else {
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// we have no other source of aiding, so use airspeed measurements to correct states
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Kfusion[0] = SK_TAS[0]*(P[0][4]*SH_TAS[2] - P[0][22]*SH_TAS[2] + P[0][5]*SK_TAS[1] - P[0][23]*SK_TAS[1] + P[0][6]*vd*SH_TAS[0]);
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Kfusion[1] = SK_TAS[0]*(P[1][4]*SH_TAS[2] - P[1][22]*SH_TAS[2] + P[1][5]*SK_TAS[1] - P[1][23]*SK_TAS[1] + P[1][6]*vd*SH_TAS[0]);
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Kfusion[2] = SK_TAS[0]*(P[2][4]*SH_TAS[2] - P[2][22]*SH_TAS[2] + P[2][5]*SK_TAS[1] - P[2][23]*SK_TAS[1] + P[2][6]*vd*SH_TAS[0]);
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Kfusion[3] = SK_TAS[0]*(P[3][4]*SH_TAS[2] - P[3][22]*SH_TAS[2] + P[3][5]*SK_TAS[1] - P[3][23]*SK_TAS[1] + P[3][6]*vd*SH_TAS[0]);
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Kfusion[4] = SK_TAS[0]*(P[4][4]*SH_TAS[2] - P[4][22]*SH_TAS[2] + P[4][5]*SK_TAS[1] - P[4][23]*SK_TAS[1] + P[4][6]*vd*SH_TAS[0]);
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Kfusion[5] = SK_TAS[0]*(P[5][4]*SH_TAS[2] - P[5][22]*SH_TAS[2] + P[5][5]*SK_TAS[1] - P[5][23]*SK_TAS[1] + P[5][6]*vd*SH_TAS[0]);
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Kfusion[6] = SK_TAS[0]*(P[6][4]*SH_TAS[2] - P[6][22]*SH_TAS[2] + P[6][5]*SK_TAS[1] - P[6][23]*SK_TAS[1] + P[6][6]*vd*SH_TAS[0]);
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Kfusion[7] = SK_TAS[0]*(P[7][4]*SH_TAS[2] - P[7][22]*SH_TAS[2] + P[7][5]*SK_TAS[1] - P[7][23]*SK_TAS[1] + P[7][6]*vd*SH_TAS[0]);
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Kfusion[8] = SK_TAS[0]*(P[8][4]*SH_TAS[2] - P[8][22]*SH_TAS[2] + P[8][5]*SK_TAS[1] - P[8][23]*SK_TAS[1] + P[8][6]*vd*SH_TAS[0]);
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Kfusion[9] = SK_TAS[0]*(P[9][4]*SH_TAS[2] - P[9][22]*SH_TAS[2] + P[9][5]*SK_TAS[1] - P[9][23]*SK_TAS[1] + P[9][6]*vd*SH_TAS[0]);
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Kfusion[10] = SK_TAS[0]*(P[10][4]*SH_TAS[2] - P[10][22]*SH_TAS[2] + P[10][5]*SK_TAS[1] - P[10][23]*SK_TAS[1] + P[10][6]*vd*SH_TAS[0]);
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Kfusion[11] = SK_TAS[0]*(P[11][4]*SH_TAS[2] - P[11][22]*SH_TAS[2] + P[11][5]*SK_TAS[1] - P[11][23]*SK_TAS[1] + P[11][6]*vd*SH_TAS[0]);
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Kfusion[12] = SK_TAS[0]*(P[12][4]*SH_TAS[2] - P[12][22]*SH_TAS[2] + P[12][5]*SK_TAS[1] - P[12][23]*SK_TAS[1] + P[12][6]*vd*SH_TAS[0]);
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Kfusion[13] = SK_TAS[0]*(P[13][4]*SH_TAS[2] - P[13][22]*SH_TAS[2] + P[13][5]*SK_TAS[1] - P[13][23]*SK_TAS[1] + P[13][6]*vd*SH_TAS[0]);
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Kfusion[14] = SK_TAS[0]*(P[14][4]*SH_TAS[2] - P[14][22]*SH_TAS[2] + P[14][5]*SK_TAS[1] - P[14][23]*SK_TAS[1] + P[14][6]*vd*SH_TAS[0]);
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Kfusion[15] = SK_TAS[0]*(P[15][4]*SH_TAS[2] - P[15][22]*SH_TAS[2] + P[15][5]*SK_TAS[1] - P[15][23]*SK_TAS[1] + P[15][6]*vd*SH_TAS[0]);
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Kfusion[16] = SK_TAS[0]*(P[16][4]*SH_TAS[2] - P[16][22]*SH_TAS[2] + P[16][5]*SK_TAS[1] - P[16][23]*SK_TAS[1] + P[16][6]*vd*SH_TAS[0]);
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Kfusion[17] = SK_TAS[0]*(P[17][4]*SH_TAS[2] - P[17][22]*SH_TAS[2] + P[17][5]*SK_TAS[1] - P[17][23]*SK_TAS[1] + P[17][6]*vd*SH_TAS[0]);
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Kfusion[18] = SK_TAS[0]*(P[18][4]*SH_TAS[2] - P[18][22]*SH_TAS[2] + P[18][5]*SK_TAS[1] - P[18][23]*SK_TAS[1] + P[18][6]*vd*SH_TAS[0]);
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Kfusion[19] = SK_TAS[0]*(P[19][4]*SH_TAS[2] - P[19][22]*SH_TAS[2] + P[19][5]*SK_TAS[1] - P[19][23]*SK_TAS[1] + P[19][6]*vd*SH_TAS[0]);
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Kfusion[20] = SK_TAS[0]*(P[20][4]*SH_TAS[2] - P[20][22]*SH_TAS[2] + P[20][5]*SK_TAS[1] - P[20][23]*SK_TAS[1] + P[20][6]*vd*SH_TAS[0]);
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Kfusion[21] = SK_TAS[0]*(P[21][4]*SH_TAS[2] - P[21][22]*SH_TAS[2] + P[21][5]*SK_TAS[1] - P[21][23]*SK_TAS[1] + P[21][6]*vd*SH_TAS[0]);
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}
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2016-04-27 23:05:54 -03:00
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Kfusion[22] = SK_TAS[0]*(P[22][4]*SH_TAS[2] - P[22][22]*SH_TAS[2] + P[22][5]*SK_TAS[1] - P[22][23]*SK_TAS[1] + P[22][6]*vd*SH_TAS[0]);
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Kfusion[23] = SK_TAS[0]*(P[23][4]*SH_TAS[2] - P[23][22]*SH_TAS[2] + P[23][5]*SK_TAS[1] - P[23][23]*SK_TAS[1] + P[23][6]*vd*SH_TAS[0]);
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2016-03-11 07:42:16 -04:00
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2016-05-08 13:21:48 -03:00
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// Calculate measurement innovation
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2016-04-05 10:14:04 -03:00
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_airspeed_innov = v_tas_pred -
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2016-05-08 13:21:48 -03:00
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_airspeed_sample_delayed.true_airspeed;
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2016-03-11 07:42:16 -04:00
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// Calculate the innovation variance
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_airspeed_innov_var = 1.0f / SK_TAS[0];
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// Compute the ratio of innovation to gate size
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_tas_test_ratio = sq(_airspeed_innov) / (sq(fmaxf(_params.tas_innov_gate, 1.0f)) * _airspeed_innov_var);
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2016-05-08 13:21:48 -03:00
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// If the innovation consistency check fails then don't fuse the sample and indicate bad airspeed health
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2016-04-05 10:14:04 -03:00
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if (_tas_test_ratio > 1.0f) {
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2016-05-23 04:33:48 -03:00
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_innov_check_fail_status.flags.reject_airspeed = true;
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2016-04-05 10:14:04 -03:00
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return;
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2017-08-23 22:18:48 -03:00
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} else {
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2016-05-23 04:33:48 -03:00
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_innov_check_fail_status.flags.reject_airspeed = false;
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2016-05-08 13:21:48 -03:00
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}
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2016-03-11 07:42:16 -04:00
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2016-05-08 13:21:48 -03:00
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// Airspeed measurement sample has passed check so record it
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2016-04-05 10:14:04 -03:00
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_time_last_arsp_fuse = _time_last_imu;
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2016-03-11 07:42:16 -04:00
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2016-07-23 20:23:50 -03:00
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// apply covariance correction via P_new = (I -K*H)*P
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// first calculate expression for KHP
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// then calculate P - KHP
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2016-11-01 08:50:29 -03:00
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float KHP[_k_num_states][_k_num_states];
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2016-11-02 06:26:40 -03:00
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float KH[5];
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2016-04-05 10:14:04 -03:00
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for (unsigned row = 0; row < _k_num_states; row++) {
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2016-11-02 06:26:40 -03:00
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KH[0] = Kfusion[row] * H_TAS[4];
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KH[1] = Kfusion[row] * H_TAS[5];
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KH[2] = Kfusion[row] * H_TAS[6];
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KH[3] = Kfusion[row] * H_TAS[22];
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KH[4] = Kfusion[row] * H_TAS[23];
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2016-04-05 10:14:04 -03:00
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for (unsigned column = 0; column < _k_num_states; column++) {
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2016-11-02 06:26:40 -03:00
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float tmp = KH[0] * P[4][column];
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tmp += KH[1] * P[5][column];
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tmp += KH[2] * P[6][column];
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tmp += KH[3] * P[22][column];
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tmp += KH[4] * P[23][column];
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2016-07-23 20:23:50 -03:00
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KHP[row][column] = tmp;
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}
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}
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2016-03-11 07:42:16 -04:00
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2016-05-07 01:23:38 -03:00
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// if the covariance correction will result in a negative variance, then
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// the covariance marix is unhealthy and must be corrected
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bool healthy = true;
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2016-05-07 08:11:16 -03:00
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_fault_status.flags.bad_airspeed = false;
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2016-05-07 01:23:38 -03:00
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for (int i = 0; i < _k_num_states; i++) {
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if (P[i][i] < KHP[i][i]) {
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// zero rows and columns
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zeroRows(P,i,i);
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zeroCols(P,i,i);
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//flag as unhealthy
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healthy = false;
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// update individual measurement health status
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2016-05-07 08:11:16 -03:00
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_fault_status.flags.bad_airspeed = true;
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2016-05-07 01:23:38 -03:00
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2016-03-11 07:42:16 -04:00
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}
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}
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2016-05-07 01:23:38 -03:00
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// only apply covariance and state corrrections if healthy
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if (healthy) {
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// apply the covariance corrections
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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];
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|
|
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}
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|
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}
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// correct the covariance marix for gross errors
|
2016-05-07 07:29:50 -03:00
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|
fixCovarianceErrors();
|
2016-05-07 01:23:38 -03:00
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|
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|
|
|
// apply the state corrections
|
|
|
|
fuse(Kfusion, _airspeed_innov);
|
|
|
|
|
|
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}
|
2016-03-11 07:42:16 -04:00
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}
|
2016-05-04 20:34:01 -03:00
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}
|
2016-07-12 06:40:41 -03:00
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|
|
|
void Ekf::get_wind_velocity(float *wind)
|
|
|
|
{
|
|
|
|
wind[0] = _state.wind_vel(0);
|
|
|
|
wind[1] = _state.wind_vel(1);
|
|
|
|
}
|
2016-07-31 01:41:48 -03:00
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|
|
|
2017-08-06 20:19:48 -03:00
|
|
|
void Ekf::get_true_airspeed(float *tas)
|
|
|
|
{
|
|
|
|
float tempvar = sqrtf(sq(_state.vel(0) - _state.wind_vel(0)) + sq(_state.vel(1) - _state.wind_vel(1)) + sq(_state.vel(2)));
|
|
|
|
memcpy(tas, &tempvar, sizeof(float));
|
|
|
|
}
|
|
|
|
|
2016-07-31 01:41:48 -03:00
|
|
|
/*
|
|
|
|
* Reset the wind states using the current airspeed measurement, ground relative nav velocity, yaw angle and assumption of zero sideslip
|
|
|
|
*/
|
|
|
|
void Ekf::resetWindStates()
|
|
|
|
{
|
|
|
|
// get euler yaw angle
|
2017-06-19 12:10:01 -03:00
|
|
|
Eulerf euler321(_state.quat_nominal);
|
2016-07-31 01:41:48 -03:00
|
|
|
float euler_yaw = euler321(2);
|
|
|
|
|
2016-10-27 01:17:02 -03:00
|
|
|
if (_tas_data_ready && (_imu_sample_delayed.time_us - _airspeed_sample_delayed.time_us < 5e5)) {
|
|
|
|
// 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);
|
|
|
|
|
|
|
|
} else {
|
|
|
|
// If we don't have an airspeed measurement, then assume the wind is zero
|
|
|
|
_state.wind_vel(0) = 0.0f;
|
|
|
|
_state.wind_vel(1) = 0.0f;
|
|
|
|
}
|
2016-07-31 01:41:48 -03:00
|
|
|
}
|