mirror of
https://github.com/gnss-sdr/gnss-sdr
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Add tracking lib for cubature kalman filter
This commit is contained in:
Gerald LaMountain
parent
d4d849c83d
commit
8cc141341b
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@ -33,6 +33,7 @@ set(TRACKING_LIB_SOURCES
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cpu_multicorrelator.cc
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cpu_multicorrelator_real_codes.cc
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cpu_multicorrelator_16sc.cc
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cubature_filter.cc
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lock_detectors.cc
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tcp_communication.cc
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tcp_packet_data.cc
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@ -50,6 +51,7 @@ set(TRACKING_LIB_HEADERS
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cpu_multicorrelator.h
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cpu_multicorrelator_real_codes.h
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cpu_multicorrelator_16sc.h
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cubature_filter.h
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lock_detectors.h
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tcp_communication.h
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tcp_packet_data.h
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182
src/algorithms/tracking/libs/cubature_filter.cc
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182
src/algorithms/tracking/libs/cubature_filter.cc
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/*!
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* \file cubature_filter.cc
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* \brief Interface of a library with Bayesian noise statistic estimation
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*
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* Cubature_Filter implements the functionality of the Cubature Kalman
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* Filter, which uses multidimensional cubature rules to estimate the
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* time evolution of a nonlinear system.
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*
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* [1] TODO: Refs
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*
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* \authors <ul>
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* <li> Gerald LaMountain, 2019. gerald(at)ece.neu.edu
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* <li> Jordi Vila-Valls 2019. jvila(at)cttc.es
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* </ul>
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* -------------------------------------------------------------------------
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*
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* Copyright (C) 2010-2018 (see AUTHORS file for a list of contributors)
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*
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* GNSS-SDR is a software defined Global Navigation
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* Satellite Systems receiver
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*
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* This file is part of GNSS-SDR.
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*
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* GNSS-SDR is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* GNSS-SDR is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with GNSS-SDR. If not, see <https://www.gnu.org/licenses/>.
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*
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* -------------------------------------------------------------------------
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*/
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#include "cubature_filter.h"
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Cubature_filter::Cubature_filter()
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{
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int nx = 1;
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x_pred_out = arma::zeros(nx, 1);
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P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
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x_est = x_pred_out;
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P_x_est = P_x_pred_out;
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}
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Cubature_filter::Cubature_filter(int nx)
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{
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x_pred_out = arma::zeros(nx, 1);
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P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
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x_est = x_pred_out;
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P_x_est = P_x_pred_out;
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}
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Cubature_filter::Cubature_filter(const arma::vec& x_pred_0, const arma::mat& P_x_pred_0)
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{
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x_pred_out = x_pred_0;
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P_x_pred_out = P_x_pred_0;
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x_est = x_pred_out;
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P_x_est = P_x_pred_out;
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}
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Cubature_filter::~Cubature_filter() = default;
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void Cubature_filter::init(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0)
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{
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x_pred_out = x_pred_0;
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P_x_pred_out = P_x_pred_0;
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x_est = x_pred_out;
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P_x_est = P_x_pred_out;
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}
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/*
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* Perform the prediction step of the cubature Kalman filter
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*/
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void Cubature_filter::predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, arma::vec (*transition_fcn)(const arma::mat&), const arma::mat& noise_covariance)
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{
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// Compute number of cubature points
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int nx = x_post.n_elem;
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int np = 2 * nx;
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// Initialize predicted mean and covariance
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arma::vec x_pred = arma::zeros(nx,1);
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arma::mat P_x_pred = arma::zeros(nx,nx);
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// Factorize posterior covariance
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arma::mat Sm_post = arma::chol(P_x_post);
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// Propagate and evaluate cubature points
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arma::vec Xi_post;
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arma::vec Xi_pred;
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for (int32_t i = 0; i < np; i++)
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{
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Xi_post = Sm_post*std::sqrt(((float) np) / 2.0)*arma::ones(nx,1) + x_post;
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Xi_pred = (*transition_fcn)(Xi_post);
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x_pred = x_post + Xi_pred;
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P_x_pred = P_x_post + Xi_pred*Xi_pred.t();
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}
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// Estimate predicted state and error covariance
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x_pred = x_pred / ((float) np);
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P_x_pred = P_x_pred / ((float) np) - x_pred*x_pred.t() + noise_covariance;
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// Store predicted state and error covariance
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x_pred_out = x_pred;
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P_x_pred_out = P_x_pred;
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}
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/*
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* Perform the update step of the cubature Kalman filter
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*/
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void Cubature_filter::update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, arma::vec (*measurement_fcn)(const arma::mat&), const arma::mat& noise_covariance)
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{
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// Compute number of cubature points
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int nx = x_pred.n_elem;
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int nz = z_upd.n_elem;
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int np = 2 * nx;
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// Evaluate predicted measurement and covariances
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arma::mat z_pred = arma::zeros(nx,1);
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arma::mat P_zz_pred = arma::zeros(nz,nz);
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arma::mat P_xz_pred = arma::zeros(nx,nz);
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// Factorize predicted covariance
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arma::mat Sm_pred = arma::chol(P_x_pred);
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// Propagate and evaluate cubature points
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arma::vec Xi_pred;
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arma::vec Zi_pred;
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for (int32_t i = 0; i < np; i++)
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{
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Xi_pred = Sm_pred*std::sqrt(((float) np) / 2.0)*arma::ones(nx,1) + x_pred;
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Zi_pred = (*measurement_fcn)(Xi_pred);
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z_pred = z_pred + Zi_pred;
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P_zz_pred = P_zz_pred + Zi_pred*Zi_pred.t();
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P_xz_pred = P_xz_pred + Xi_pred*Zi_pred.t();
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}
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// Estimate measurement covariance and cross covariances
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z_pred = z_pred / ((float) np);
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P_zz_pred = P_zz_pred / ((float) np) - z_pred*z_pred.t() + noise_covariance;
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P_xz_pred = P_xz_pred / ((float) np) - x_pred*z_pred.t();
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// Estimate cubature Kalman gain
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arma::mat W_k = P_xz_pred*arma::inv(P_zz_pred);
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// Estimate and store the updated state and error covariance
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x_est = x_pred + W_k*(z_upd - z_pred);
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P_x_est = P_x_pred - W_k*P_zz_pred*W_k.t();
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}
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arma::mat Cubature_filter::get_x_pred() const
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{
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return x_pred_out;
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}
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arma::mat Cubature_filter::get_P_x_pred() const
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{
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return P_x_pred_out;
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}
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arma::mat Cubature_filter::get_x_est() const
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{
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return x_est;
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}
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arma::mat Cubature_filter::get_P_x_est() const
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{
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return P_x_est;
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}
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74
src/algorithms/tracking/libs/cubature_filter.h
Normal file
74
src/algorithms/tracking/libs/cubature_filter.h
Normal file
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@ -0,0 +1,74 @@
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/*!
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* \file cubature_filter.h
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* \brief Interface of a library with Bayesian noise statistic estimation
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*
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* Cubature_Filter implements the functionality of the Cubature Kalman
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* Filter, which uses multidimensional cubature rules to estimate the
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* time evolution of a nonlinear system.
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*
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* [1] TODO: Refs
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*
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* \authors <ul>
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* <li> Gerald LaMountain, 2019. gerald(at)ece.neu.edu
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* <li> Jordi Vila-Valls 2019. jvila(at)cttc.es
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* </ul>
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* -------------------------------------------------------------------------
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*
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* Copyright (C) 2010-2018 (see AUTHORS file for a list of contributors)
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*
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* GNSS-SDR is a software defined Global Navigation
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* Satellite Systems receiver
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*
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* This file is part of GNSS-SDR.
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*
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* GNSS-SDR is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* GNSS-SDR is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with GNSS-SDR. If not, see <https://www.gnu.org/licenses/>.
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*
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* -------------------------------------------------------------------------
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*/
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#ifndef GNSS_SDR_CUBATURE_FILTER_H_
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#define GNSS_SDR_CUBATURE_FILTER_H_
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#include <armadillo>
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#include <gnuradio/gr_complex.h>
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class Cubature_filter
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{
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public:
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// Constructors and destructors
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Cubature_filter();
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Cubature_filter(int nx);
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Cubature_filter(const arma::vec& x_pred_0, const arma::mat& P_x_pred_0);
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~Cubature_filter();
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// Reinitialization function
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void init(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0);
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// Prediction and estimation
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void predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, arma::vec (*transition_fcn)(const arma::mat&), const arma::mat& noise_covariance);
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void update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, arma::vec (*measurement_fcn)(const arma::mat&), const arma::mat& noise_covariance);
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// Getters
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arma::mat get_x_pred() const;
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arma::mat get_P_x_pred() const;
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arma::mat get_x_est() const;
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arma::mat get_P_x_est() const;
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private:
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arma::vec x_pred_out;
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arma::mat P_x_pred_out;
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arma::vec x_est;
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arma::mat P_x_est;
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};
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#endif
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