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Merge branch 'next' of https://github.com/gnss-sdr/gnss-sdr into tracking_debug

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This commit is contained in:
Javier Arribas
2019-06-14 12:39:25 +02:00
parent 5a43e757dd d389525af4
commit afc2a98089
8 changed files with 603 additions and 216 deletions
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4
src/algorithms/tracking/libs/CMakeLists.txt
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@@ -33,7 +33,7 @@ set(TRACKING_LIB_SOURCES
cpu_multicorrelator.cc
cpu_multicorrelator_real_codes.cc
cpu_multicorrelator_16sc.cc
cubature_filter.cc
nonlinear_tracking.cc
lock_detectors.cc
tcp_communication.cc
tcp_packet_data.cc
@@ -51,7 +51,7 @@ set(TRACKING_LIB_HEADERS
cpu_multicorrelator.h
cpu_multicorrelator_real_codes.h
cpu_multicorrelator_16sc.h
cubature_filter.h
nonlinear_tracking.h
lock_detectors.h
tcp_communication.h
tcp_packet_data.h

199
src/algorithms/tracking/libs/cubature_filter.cc
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@@ -1,199 +0,0 @@
/*!
* \file cubature_filter.cc
* \brief Interface of a library with Bayesian noise statistic estimation
*
* Cubature_Filter implements the functionality of the Cubature Kalman
* Filter, which uses multidimensional cubature rules to estimate the
* time evolution of a nonlinear system.
*
* [1] I Arasaratnam and S Haykin. Cubature kalman filters. IEEE
* Transactions on Automatic Control, 54(6):1254–1269,2009.
*
* \authors <ul>
* <li> Gerald LaMountain, 2019. gerald(at)ece.neu.edu
* <li> Jordi Vila-Valls 2019. jvila(at)cttc.es
* </ul>
* -------------------------------------------------------------------------
*
* Copyright (C) 2010-2019 (see AUTHORS file for a list of contributors)
*
* GNSS-SDR is a software defined Global Navigation
* Satellite Systems receiver
*
* This file is part of GNSS-SDR.
*
* GNSS-SDR is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* GNSS-SDR is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with GNSS-SDR. If not, see <https://www.gnu.org/licenses/>.
*
* -------------------------------------------------------------------------
*/
#include "cubature_filter.h"
Cubature_filter::Cubature_filter()
{
int nx = 1;
x_pred_out = arma::zeros(nx, 1);
P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Cubature_filter::Cubature_filter(int nx)
{
x_pred_out = arma::zeros(nx, 1);
P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Cubature_filter::Cubature_filter(const arma::vec& x_pred_0, const arma::mat& P_x_pred_0)
{
x_pred_out = x_pred_0;
P_x_pred_out = P_x_pred_0;
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Cubature_filter::~Cubature_filter() = default;
void Cubature_filter::initialize(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0)
{
x_pred_out = x_pred_0;
P_x_pred_out = P_x_pred_0;
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
/*
* Perform the prediction step of the cubature Kalman filter
*/
void Cubature_filter::predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, Model_Function* transition_fcn, const arma::mat& noise_covariance)
{
// Compute number of cubature points
int nx = x_post.n_elem;
int np = 2 * nx;
// Generator Matrix
arma::mat gen_one = arma::join_horiz(arma::eye(nx, nx), -1.0 * arma::eye(nx, nx));
// Initialize predicted mean and covariance
arma::vec x_pred = arma::zeros(nx, 1);
arma::mat P_x_pred = arma::zeros(nx, nx);
// Factorize posterior covariance
arma::mat Sm_post = arma::chol(P_x_post, "lower");
// Propagate and evaluate cubature points
arma::vec Xi_post;
arma::vec Xi_pred;
for (uint8_t i = 0; i < np; i++)
{
Xi_post = Sm_post * (std::sqrt(static_cast<float>(np) / 2.0) * gen_one.col(i)) + x_post;
Xi_pred = (*transition_fcn)(Xi_post);
x_pred = x_pred + Xi_pred;
P_x_pred = P_x_pred + Xi_pred * Xi_pred.t();
}
// Estimate predicted state and error covariance
x_pred = x_pred / static_cast<float>(np);
P_x_pred = P_x_pred / static_cast<float>(np) - x_pred * x_pred.t() + noise_covariance;
// Store predicted state and error covariance
x_pred_out = x_pred;
P_x_pred_out = P_x_pred;
}
/*
* Perform the update step of the cubature Kalman filter
*/
void Cubature_filter::update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, Model_Function* measurement_fcn, const arma::mat& noise_covariance)
{
// Compute number of cubature points
int nx = x_pred.n_elem;
int nz = z_upd.n_elem;
int np = 2 * nx;
// Generator Matrix
arma::mat gen_one = arma::join_horiz(arma::eye(nx, nx), -1.0 * arma::eye(nx, nx));
// Evaluate predicted measurement and covariances
arma::mat z_pred = arma::zeros(nz, 1);
arma::mat P_zz_pred = arma::zeros(nz, nz);
arma::mat P_xz_pred = arma::zeros(nx, nz);
// Factorize predicted covariance
arma::mat Sm_pred = arma::chol(P_x_pred, "lower");
// Propagate and evaluate cubature points
arma::vec Xi_pred;
arma::vec Zi_pred;
for (uint8_t i = 0; i < np; i++)
{
Xi_pred = Sm_pred * (std::sqrt(static_cast<float>(np) / 2.0) * gen_one.col(i)) + x_pred;
Zi_pred = (*measurement_fcn)(Xi_pred);
z_pred = z_pred + Zi_pred;
P_zz_pred = P_zz_pred + Zi_pred * Zi_pred.t();
P_xz_pred = P_xz_pred + Xi_pred * Zi_pred.t();
}
// Estimate measurement covariance and cross covariances
z_pred = z_pred / static_cast<float>(np);
P_zz_pred = P_zz_pred / static_cast<float>(np) - z_pred * z_pred.t() + noise_covariance;
P_xz_pred = P_xz_pred / static_cast<float>(np) - x_pred * z_pred.t();
// Estimate cubature Kalman gain
arma::mat W_k = P_xz_pred * arma::inv(P_zz_pred);
// Estimate and store the updated state and error covariance
x_est = x_pred + W_k * (z_upd - z_pred);
P_x_est = P_x_pred - W_k * P_zz_pred * W_k.t();
}
arma::mat Cubature_filter::get_x_pred() const
{
return x_pred_out;
}
arma::mat Cubature_filter::get_P_x_pred() const
{
return P_x_pred_out;
}
arma::mat Cubature_filter::get_x_est() const
{
return x_est;
}
arma::mat Cubature_filter::get_P_x_est() const
{
return P_x_est;
}

390
src/algorithms/tracking/libs/nonlinear_tracking.cc Normal file
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@@ -0,0 +1,390 @@
/*!
* \file cubature_filter.cc
* \brief Interface of a library for nonlinear tracking algorithms
*
* Cubature_Filter implements the functionality of the Cubature Kalman
* Filter, which uses multidimensional cubature rules to estimate the
* time evolution of a nonlinear system. Unscented_filter implements
* an Unscented Kalman Filter which uses Unscented Transform rules to
* perform a similar estimation.
*
* [1] I Arasaratnam and S Haykin. Cubature kalman filters. IEEE
* Transactions on Automatic Control, 54(6):1254–1269,2009.
*
* \authors <ul>
* <li> Gerald LaMountain, 2019. gerald(at)ece.neu.edu
* <li> Jordi Vila-Valls 2019. jvila(at)cttc.es
* </ul>
* -------------------------------------------------------------------------
*
* Copyright (C) 2010-2019 (see AUTHORS file for a list of contributors)
*
* GNSS-SDR is a software defined Global Navigation
* Satellite Systems receiver
*
* This file is part of GNSS-SDR.
*
* GNSS-SDR is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* GNSS-SDR is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with GNSS-SDR. If not, see <https://www.gnu.org/licenses/>.
*
* -------------------------------------------------------------------------
*/
#include "nonlinear_tracking.h"
/***************** CUBATURE KALMAN FILTER *****************/
Cubature_filter::Cubature_filter()
{
int nx = 1;
x_pred_out = arma::zeros(nx, 1);
P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Cubature_filter::Cubature_filter(int nx)
{
x_pred_out = arma::zeros(nx, 1);
P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Cubature_filter::Cubature_filter(const arma::vec& x_pred_0, const arma::mat& P_x_pred_0)
{
x_pred_out = x_pred_0;
P_x_pred_out = P_x_pred_0;
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Cubature_filter::~Cubature_filter() = default;
void Cubature_filter::initialize(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0)
{
x_pred_out = x_pred_0;
P_x_pred_out = P_x_pred_0;
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
/*
* Perform the prediction step of the cubature Kalman filter
*/
void Cubature_filter::predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, Model_Function* transition_fcn, const arma::mat& noise_covariance)
{
// Compute number of cubature points
int nx = x_post.n_elem;
int np = 2 * nx;
// Generator Matrix
arma::mat gen_one = arma::join_horiz(arma::eye(nx, nx), -1.0 * arma::eye(nx, nx));
// Initialize predicted mean and covariance
arma::vec x_pred = arma::zeros(nx, 1);
arma::mat P_x_pred = arma::zeros(nx, nx);
// Factorize posterior covariance
arma::mat Sm_post = arma::chol(P_x_post, "lower");
// Propagate and evaluate cubature points
arma::vec Xi_post;
arma::vec Xi_pred;
for (uint8_t i = 0; i < np; i++)
{
Xi_post = Sm_post * (std::sqrt(static_cast<float>(np) / 2.0) * gen_one.col(i)) + x_post;
Xi_pred = (*transition_fcn)(Xi_post);
x_pred = x_pred + Xi_pred;
P_x_pred = P_x_pred + Xi_pred * Xi_pred.t();
}
// Compute predicted mean and error covariance
x_pred = x_pred / static_cast<float>(np);
P_x_pred = P_x_pred / static_cast<float>(np) - x_pred * x_pred.t() + noise_covariance;
// Store predicted mean and error covariance
x_pred_out = x_pred;
P_x_pred_out = P_x_pred;
}
/*
* Perform the update step of the cubature Kalman filter
*/
void Cubature_filter::update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, Model_Function* measurement_fcn, const arma::mat& noise_covariance)
{
// Compute number of cubature points
int nx = x_pred.n_elem;
int nz = z_upd.n_elem;
int np = 2 * nx;
// Generator Matrix
arma::mat gen_one = arma::join_horiz(arma::eye(nx, nx), -1.0 * arma::eye(nx, nx));
// Initialize estimated predicted measurement and covariances
arma::mat z_pred = arma::zeros(nz, 1);
arma::mat P_zz_pred = arma::zeros(nz, nz);
arma::mat P_xz_pred = arma::zeros(nx, nz);
// Factorize predicted covariance
arma::mat Sm_pred = arma::chol(P_x_pred, "lower");
// Propagate and evaluate cubature points
arma::vec Xi_pred;
arma::vec Zi_pred;
for (uint8_t i = 0; i < np; i++)
{
Xi_pred = Sm_pred * (std::sqrt(static_cast<float>(np) / 2.0) * gen_one.col(i)) + x_pred;
Zi_pred = (*measurement_fcn)(Xi_pred);
z_pred = z_pred + Zi_pred;
P_zz_pred = P_zz_pred + Zi_pred * Zi_pred.t();
P_xz_pred = P_xz_pred + Xi_pred * Zi_pred.t();
}
// Compute measurement mean, covariance and cross covariance
z_pred = z_pred / static_cast<float>(np);
P_zz_pred = P_zz_pred / static_cast<float>(np) - z_pred * z_pred.t() + noise_covariance;
P_xz_pred = P_xz_pred / static_cast<float>(np) - x_pred * z_pred.t();
// Compute cubature Kalman gain
arma::mat W_k = P_xz_pred * arma::inv(P_zz_pred);
// Compute and store the updated mean and error covariance
x_est = x_pred + W_k * (z_upd - z_pred);
P_x_est = P_x_pred - W_k * P_zz_pred * W_k.t();
}
arma::mat Cubature_filter::get_x_pred() const
{
return x_pred_out;
}
arma::mat Cubature_filter::get_P_x_pred() const
{
return P_x_pred_out;
}
arma::mat Cubature_filter::get_x_est() const
{
return x_est;
}
arma::mat Cubature_filter::get_P_x_est() const
{
return P_x_est;
}
/***************** END CUBATURE KALMAN FILTER *****************/
/***************** UNSCENTED KALMAN FILTER *****************/
Unscented_filter::Unscented_filter()
{
int nx = 1;
x_pred_out = arma::zeros(nx, 1);
P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Unscented_filter::Unscented_filter(int nx)
{
x_pred_out = arma::zeros(nx, 1);
P_x_pred_out = arma::eye(nx, nx) * (nx + 1);
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Unscented_filter::Unscented_filter(const arma::vec& x_pred_0, const arma::mat& P_x_pred_0)
{
x_pred_out = x_pred_0;
P_x_pred_out = P_x_pred_0;
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
Unscented_filter::~Unscented_filter() = default;
void Unscented_filter::initialize(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0)
{
x_pred_out = x_pred_0;
P_x_pred_out = P_x_pred_0;
x_est = x_pred_out;
P_x_est = P_x_pred_out;
}
/*
* Perform the prediction step of the Unscented Kalman filter
*/
void Unscented_filter::predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, Model_Function* transition_fcn, const arma::mat& noise_covariance)
{
// Compute number of sigma points
int nx = x_post.n_elem;
int np = 2 * nx + 1;
float alpha = 0.001;
float kappa = 0.0;
float beta = 2.0;
float lambda = std::pow(alpha, 2.0) * (static_cast<float>(nx) + kappa) - static_cast<float>(nx);
// Compute UT Weights
float W0_m = lambda / (static_cast<float>(nx) + lambda);
float W0_c = lambda / (static_cast<float>(nx) + lambda) + (1 - std::pow(alpha, 2.0) + beta);
float Wi_m = 1.0 / (2.0 * (static_cast<float>(nx) + lambda));
// Propagate and evaluate sigma points
arma::mat Xi_fact = arma::zeros(nx, nx);
arma::mat Xi_post = arma::zeros(nx, np);
arma::mat Xi_pred = arma::zeros(nx, np);
Xi_post.col(0) = x_post;
Xi_pred.col(0) = (*transition_fcn)(Xi_post.col(0));
for (uint8_t i = 1; i <= nx; i++)
{
Xi_fact = std::sqrt(static_cast<float>(nx) + lambda) * arma::sqrtmat_sympd(P_x_post);
Xi_post.col(i) = x_post + Xi_fact.col(i - 1);
Xi_post.col(i + nx) = x_post - Xi_fact.col(i - 1);
Xi_pred.col(i) = (*transition_fcn)(Xi_post.col(i));
Xi_pred.col(i + nx) = (*transition_fcn)(Xi_post.col(i + nx));
}
// Compute predicted mean
arma::vec x_pred = W0_m * Xi_pred.col(0) + Wi_m * arma::sum(Xi_pred.cols(1, np - 1), 1);
// Compute predicted error covariance
arma::mat P_x_pred = W0_c * ((Xi_pred.col(0) - x_pred) * (Xi_pred.col(0).t() - x_pred.t()));
for (uint8_t i = 1; i < np; i++)
{
P_x_pred = P_x_pred + Wi_m * ((Xi_pred.col(i) - x_pred) * (Xi_pred.col(i).t() - x_pred.t()));
}
P_x_pred = P_x_pred + noise_covariance;
// Store predicted mean and error covariance
x_pred_out = x_pred;
P_x_pred_out = P_x_pred;
}
/*
* Perform the update step of the Unscented Kalman filter
*/
void Unscented_filter::update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, Model_Function* measurement_fcn, const arma::mat& noise_covariance)
{
// Compute number of sigma points
int nx = x_pred.n_elem;
int nz = z_upd.n_elem;
int np = 2 * nx + 1;
float alpha = 0.001;
float kappa = 0.0;
float beta = 2.0;
float lambda = std::pow(alpha, 2.0) * (static_cast<float>(nx) + kappa) - static_cast<float>(nx);
// Compute UT Weights
float W0_m = lambda / (static_cast<float>(nx) + lambda);
float W0_c = lambda / (static_cast<float>(nx) + lambda) + (1.0 - std::pow(alpha, 2.0) + beta);
float Wi_m = 1.0 / (2.0 * (static_cast<float>(nx) + lambda));
// Propagate and evaluate sigma points
arma::mat Xi_fact = arma::zeros(nx, nx);
arma::mat Xi_pred = arma::zeros(nx, np);
arma::mat Zi_pred = arma::zeros(nz, np);
Xi_pred.col(0) = x_pred;
Zi_pred.col(0) = (*measurement_fcn)(Xi_pred.col(0));
for (uint8_t i = 1; i <= nx; i++)
{
Xi_fact = std::sqrt(static_cast<float>(nx) + lambda) * arma::sqrtmat_sympd(P_x_pred);
Xi_pred.col(i) = x_pred + Xi_fact.col(i - 1);
Xi_pred.col(i + nx) = x_pred - Xi_fact.col(i - 1);
Zi_pred.col(i) = (*measurement_fcn)(Xi_pred.col(i));
Zi_pred.col(i + nx) = (*measurement_fcn)(Xi_pred.col(i + nx));
}
// Compute measurement mean
arma::mat z_pred = W0_m * Zi_pred.col(0) + Wi_m * arma::sum(Zi_pred.cols(1, np - 1), 1);
// Compute measurement covariance and cross covariance
arma::mat P_zz_pred = W0_c * ((Zi_pred.col(0) - z_pred) * (Zi_pred.col(0).t() - z_pred.t()));
arma::mat P_xz_pred = W0_c * ((Xi_pred.col(0) - x_pred) * (Zi_pred.col(0).t() - z_pred.t()));
for (uint8_t i = 0; i < np; i++)
{
P_zz_pred = P_zz_pred + Wi_m * ((Zi_pred.col(i) - z_pred) * (Zi_pred.col(i).t() - z_pred.t()));
P_xz_pred = P_xz_pred + Wi_m * ((Xi_pred.col(i) - x_pred) * (Zi_pred.col(i).t() - z_pred.t()));
}
P_zz_pred = P_zz_pred + noise_covariance;
// Estimate cubature Kalman gain
arma::mat W_k = P_xz_pred * arma::inv(P_zz_pred);
// Estimate and store the updated mean and error covariance
x_est = x_pred + W_k * (z_upd - z_pred);
P_x_est = P_x_pred - W_k * P_zz_pred * W_k.t();
}
arma::mat Unscented_filter::get_x_pred() const
{
return x_pred_out;
}
arma::mat Unscented_filter::get_P_x_pred() const
{
return P_x_pred_out;
}
arma::mat Unscented_filter::get_x_est() const
{
return x_est;
}
arma::mat Unscented_filter::get_P_x_est() const
{
return P_x_est;
}
/***************** END UNSCENTED KALMAN FILTER *****************/

52
src/algorithms/tracking/libs/cubature_filter.h → src/algorithms/tracking/libs/nonlinear_tracking.h
View File

@@ -1,10 +1,12 @@
/*!
* \file cubature_filter.h
* \brief Interface of a library with Bayesian noise statistic estimation
* \file nonlinear_tracking.h
* \brief Interface of a library for nonlinear tracking algorithms
*
* Cubature_Filter implements the functionality of the Cubature Kalman
* Filter, which uses multidimensional cubature rules to estimate the
* time evolution of a nonlinear system.
* time evolution of a nonlinear system. Unscented_filter implements
* an Unscented Kalman Filter which uses Unscented Transform rules to
* perform a similar estimation.
*
* [1] I Arasaratnam and S Haykin. Cubature kalman filters. IEEE
* Transactions on Automatic Control, 54(6):1254–1269,2009.
@@ -38,18 +40,19 @@
* -------------------------------------------------------------------------
*/
#ifndef GNSS_SDR_CUBATURE_FILTER_H_
#define GNSS_SDR_CUBATURE_FILTER_H_
#ifndef GNSS_SDR_NONLINEAR_TRACKING_H_
#define GNSS_SDR_NONLINEAR_TRACKING_H_
#include <armadillo>
#include <gnuradio/gr_complex.h>
// Abstract model function
class Model_Function{
public:
Model_Function() {};
virtual arma::vec operator() (arma::vec input) = 0;
virtual ~Model_Function() = default;
class Model_Function
{
public:
Model_Function(){};
virtual arma::vec operator()(arma::vec input) = 0;
virtual ~Model_Function() = default;
};
class Cubature_filter
@@ -81,4 +84,33 @@ private:
arma::mat P_x_est;
};
class Unscented_filter
{
public:
// Constructors and destructors
Unscented_filter();
Unscented_filter(int nx);
Unscented_filter(const arma::vec& x_pred_0, const arma::mat& P_x_pred_0);
~Unscented_filter();
// Reinitialization function
void initialize(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0);
// Prediction and estimation
void predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, Model_Function* transition_fcn, const arma::mat& noise_covariance);
void update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, Model_Function* measurement_fcn, const arma::mat& noise_covariance);
// Getters
arma::mat get_x_pred() const;
arma::mat get_P_x_pred() const;
arma::mat get_x_est() const;
arma::mat get_P_x_est() const;
private:
arma::vec x_pred_out;
arma::mat P_x_pred_out;
arma::vec x_est;
arma::mat P_x_est;
};
#endif