diff --git a/src/algorithms/tracking/libs/cubature_filter.cc b/src/algorithms/tracking/libs/cubature_filter.cc
index 19dcdbcab..d3d32350e 100644
--- a/src/algorithms/tracking/libs/cubature_filter.cc
+++ b/src/algorithms/tracking/libs/cubature_filter.cc
@@ -6,7 +6,8 @@
* Filter, which uses multidimensional cubature rules to estimate the
* time evolution of a nonlinear system.
*
- * [1] TODO: Refs
+ * [1] I Arasaratnam and S Haykin. Cubature kalman filters. IEEE
+ * Transactions on Automatic Control, 54(6):1254–1269,2009.
*
* \authors
* - Gerald LaMountain, 2019. gerald(at)ece.neu.edu
@@ -14,7 +15,7 @@
*
* -------------------------------------------------------------------------
*
- * Copyright (C) 2010-2018 (see AUTHORS file for a list of contributors)
+ * Copyright (C) 2010-2019 (see AUTHORS file for a list of contributors)
*
* GNSS-SDR is a software defined Global Navigation
* Satellite Systems receiver
@@ -70,7 +71,7 @@ Cubature_filter::Cubature_filter(const arma::vec& x_pred_0, const arma::mat& P_x
Cubature_filter::~Cubature_filter() = default;
-void Cubature_filter::init(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0)
+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;
@@ -83,29 +84,33 @@ void Cubature_filter::init(const arma::mat& x_pred_0, const arma::mat& P_x_pred_
/*
* 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, arma::vec (*transition_fcn)(const arma::mat&), const arma::mat& noise_covariance)
+void Cubature_filter::predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, ModelFunction* 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);
+ arma::mat Sm_post = arma::chol(P_x_post, "lower");
// Propagate and evaluate cubature points
arma::vec Xi_post;
arma::vec Xi_pred;
- for (int32_t i = 0; i < np; i++)
+
+ for (uint8_t i = 0; i < np; i++)
{
- Xi_post = Sm_post*std::sqrt(((float) np) / 2.0)*arma::ones(nx,1) + x_post;
+ Xi_post = Sm_post * (std::sqrt(((float) np) / 2.0) * gen_one.col(i)) + x_post;
Xi_pred = (*transition_fcn)(Xi_post);
-
- x_pred = x_post + Xi_pred;
- P_x_pred = P_x_post + Xi_pred*Xi_pred.t();
+
+ x_pred = x_pred + Xi_pred;
+ P_x_pred = P_x_pred + Xi_pred*Xi_pred.t();
}
// Estimate predicted state and error covariance
@@ -120,34 +125,37 @@ void Cubature_filter::predict_sequential(const arma::vec& x_post, const arma::ma
/*
* 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, arma::vec (*measurement_fcn)(const arma::mat&), const arma::mat& noise_covariance)
+void Cubature_filter::update_sequential(const arma::vec& z_upd, const arma::vec& x_pred, const arma::mat& P_x_pred, ModelFunction* 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(nx,1);
+ 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);
+ arma::mat Sm_pred = arma::chol(P_x_pred, "lower");
// Propagate and evaluate cubature points
arma::vec Xi_pred;
arma::vec Zi_pred;
- for (int32_t i = 0; i < np; i++)
+ for (uint8_t i = 0; i < np; i++)
{
- Xi_pred = Sm_pred*std::sqrt(((float) np) / 2.0)*arma::ones(nx,1) + x_pred;
+ Xi_pred = Sm_pred * (std::sqrt(((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 / ((float) np);
P_zz_pred = P_zz_pred / ((float) np) - z_pred*z_pred.t() + noise_covariance;
@@ -180,3 +188,8 @@ arma::mat Cubature_filter::get_P_x_est() const
{
return P_x_est;
}
+
+double Cubature_filter::func_number(double number, TestModel* func)
+{
+ return (*func)(number);
+}
diff --git a/src/algorithms/tracking/libs/cubature_filter.h b/src/algorithms/tracking/libs/cubature_filter.h
index 6c3c78968..f4b31c43a 100644
--- a/src/algorithms/tracking/libs/cubature_filter.h
+++ b/src/algorithms/tracking/libs/cubature_filter.h
@@ -6,7 +6,8 @@
* Filter, which uses multidimensional cubature rules to estimate the
* time evolution of a nonlinear system.
*
- * [1] TODO: Refs
+ * [1] I Arasaratnam and S Haykin. Cubature kalman filters. IEEE
+ * Transactions on Automatic Control, 54(6):1254–1269,2009.
*
* \authors
* - Gerald LaMountain, 2019. gerald(at)ece.neu.edu
@@ -14,7 +15,7 @@
*
* -------------------------------------------------------------------------
*
- * Copyright (C) 2010-2018 (see AUTHORS file for a list of contributors)
+ * Copyright (C) 2010-2019 (see AUTHORS file for a list of contributors)
*
* GNSS-SDR is a software defined Global Navigation
* Satellite Systems receiver
@@ -43,6 +44,22 @@
#include
#include
+// Abstract model function
+class ModelFunction{
+ public:
+ ModelFunction() {};
+ virtual arma::vec operator() (arma::vec input) = 0;
+ virtual ~ModelFunction() = default;
+};
+
+class TestModel{
+ public:
+ TestModel() {};
+ //virtual arma::vec operator() (arma::vec input) = 0;
+ virtual double operator() (double input) = 0;
+ virtual ~TestModel() = default;
+};
+
class Cubature_filter
{
public:
@@ -53,17 +70,21 @@ public:
~Cubature_filter();
// Reinitialization function
- void init(const arma::mat& x_pred_0, const arma::mat& P_x_pred_0);
+ 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, arma::vec (*transition_fcn)(const arma::mat&), const arma::mat& noise_covariance);
- 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);
+ void predict_sequential(const arma::vec& x_post, const arma::mat& P_x_post, ModelFunction* 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, ModelFunction* 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;
+
+ //Test-dev
+ double func_number(double number, TestModel* func);
+
private:
arma::vec x_pred_out;
arma::mat P_x_pred_out;
diff --git a/src/tests/CMakeLists.txt b/src/tests/CMakeLists.txt
index 3053df1d4..51e67077f 100644
--- a/src/tests/CMakeLists.txt
+++ b/src/tests/CMakeLists.txt
@@ -755,6 +755,7 @@ if(NOT ENABLE_PACKAGING AND NOT ENABLE_FPGA)
${CMAKE_CURRENT_SOURCE_DIR}/unit-tests/signal-processing-blocks/tracking/tracking_loop_filter_test.cc
${CMAKE_CURRENT_SOURCE_DIR}/unit-tests/signal-processing-blocks/tracking/cpu_multicorrelator_real_codes_test.cc
${CMAKE_CURRENT_SOURCE_DIR}/unit-tests/signal-processing-blocks/tracking/bayesian_estimation_test.cc
+ ${CMAKE_CURRENT_SOURCE_DIR}/unit-tests/signal-processing-blocks/tracking/cubature_filter_test.cc
)
target_link_libraries(trk_test
diff --git a/src/tests/test_main.cc b/src/tests/test_main.cc
index 05df98d35..d22149fee 100644
--- a/src/tests/test_main.cc
+++ b/src/tests/test_main.cc
@@ -99,6 +99,7 @@ DECLARE_string(log_dir);
#endif
#include "unit-tests/signal-processing-blocks/tracking/bayesian_estimation_test.cc"
+#include "unit-tests/signal-processing-blocks/tracking/cubature_filter_test.cc"
#include "unit-tests/signal-processing-blocks/tracking/cpu_multicorrelator_real_codes_test.cc"
#include "unit-tests/signal-processing-blocks/tracking/cpu_multicorrelator_test.cc"
#include "unit-tests/signal-processing-blocks/tracking/galileo_e1_dll_pll_veml_tracking_test.cc"
diff --git a/src/tests/unit-tests/signal-processing-blocks/tracking/bayesian_estimation_test.cc b/src/tests/unit-tests/signal-processing-blocks/tracking/bayesian_estimation_test.cc
index dd7d0398b..6fa5f1da0 100644
--- a/src/tests/unit-tests/signal-processing-blocks/tracking/bayesian_estimation_test.cc
+++ b/src/tests/unit-tests/signal-processing-blocks/tracking/bayesian_estimation_test.cc
@@ -1,7 +1,7 @@
/*!
- * \file bayesian_estimation_positivity_test.cc
- * \brief This file implements timing tests for the Bayesian covariance estimator
- * \author Gerald LaMountain, 20168. gerald(at)ece.neu.edu
+ * \file bayesian_estimation_test.cc
+ * \brief This file implements feasability test for the BCE library.
+ * \author Gerald LaMountain, 2018. gerald(at)ece.neu.edu
*
*
* -------------------------------------------------------------------------
diff --git a/src/tests/unit-tests/signal-processing-blocks/tracking/cubature_filter_test.cc b/src/tests/unit-tests/signal-processing-blocks/tracking/cubature_filter_test.cc
new file mode 100644
index 000000000..e73bb1d45
--- /dev/null
+++ b/src/tests/unit-tests/signal-processing-blocks/tracking/cubature_filter_test.cc
@@ -0,0 +1,156 @@
+/*!
+ * \file cubature_filter_test.cc
+ * \brief This file implements numerical accuracy test for the CKF library.
+ * \author Gerald LaMountain, 2019. gerald(at)ece.neu.edu
+ *
+ * -------------------------------------------------------------------------
+ *
+ * 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 .
+ *
+ * -------------------------------------------------------------------------
+ */
+
+#include "cubature_filter.h"
+#include
+#include
+#include
+
+#define CUBATURE_TEST_N_TRIALS 1000
+
+class TransitionModel : public ModelFunction {
+ public:
+ TransitionModel(arma::mat kf_F) {coeff_mat = kf_F;};
+ virtual arma::vec operator() (arma::vec input) {return coeff_mat*input;};
+ private:
+ arma::mat coeff_mat;
+};
+
+class MeasurementModel : public ModelFunction {
+ public:
+ MeasurementModel(arma::mat kf_H) {coeff_mat = kf_H;};
+ virtual arma::vec operator() (arma::vec input) {return coeff_mat*input;};
+ private:
+ arma::mat coeff_mat;
+};
+
+TEST(CubatureFilterComputationTest, CubatureFilterTest)
+{
+ Cubature_filter kf_cubature;
+
+ arma::vec kf_x;
+ arma::mat kf_P_x;
+
+ arma::vec kf_x_pre;
+ arma::mat kf_P_x_pre;
+
+ arma::vec ckf_x_pre;
+ arma::mat ckf_P_x_pre;
+
+ arma::vec kf_x_post;
+ arma::mat kf_P_x_post;
+
+ arma::vec ckf_x_post;
+ arma::mat ckf_P_x_post;
+
+ arma::mat kf_F;
+ arma::mat kf_H;
+
+ arma::mat kf_Q;
+ arma::mat kf_R;
+
+ arma::vec eta;
+ arma::vec nu;
+
+ arma::vec kf_y;
+ arma::mat kf_P_y;
+ arma::mat kf_K;
+
+ ModelFunction* transition_function;
+ ModelFunction* measurement_function;
+
+ //--- Perform initializations ------------------------------
+
+ std::random_device r;
+ std::default_random_engine e1(r());
+ std::normal_distribution normal_dist(0, 5);
+ std::uniform_real_distribution uniform_dist(0.1, 5.0);
+
+ uint8_t nx = 0;
+ uint8_t ny = 0;
+
+ for (uint16_t k = 0; k < CUBATURE_TEST_N_TRIALS; k++)
+ {
+ nx = std::rand() % 5 + 1;
+ ny = std::rand() % 5 + 1;
+
+ kf_x = arma::randn(nx,1);
+
+ kf_P_x_post = 5.0 * arma::diagmat(arma::randu(nx,1));
+ kf_x_post = arma::mvnrnd(kf_x, kf_P_x_post);
+
+ kf_cubature.initialize(kf_x_post, kf_P_x_post);
+
+ // Prediction Step
+ kf_F = arma::randu(nx,nx);
+ kf_Q = arma::diagmat(arma::randu(nx,1));
+
+ transition_function = new TransitionModel(kf_F);
+ arma::mat ttx = (*transition_function)(kf_x_post);
+
+ kf_cubature.predict_sequential(kf_x_post,kf_P_x_post,transition_function,kf_Q);
+
+ ckf_x_pre = kf_cubature.get_x_pred();
+ ckf_P_x_pre = kf_cubature.get_P_x_pred();
+
+ kf_x_pre = kf_F * kf_x_post;
+ kf_P_x_pre = kf_F * kf_P_x_post * kf_F.t() + kf_Q;
+
+ EXPECT_TRUE(arma::approx_equal(ckf_x_pre, kf_x_pre, "absdiff", 0.01));
+ EXPECT_TRUE(arma::approx_equal(ckf_P_x_pre, kf_P_x_pre, "absdiff", 0.01));
+
+ // Update Step
+ kf_H = arma::randu(ny,nx);
+ kf_R = arma::diagmat(arma::randu(ny,1));
+
+ eta = arma::mvnrnd(arma::zeros(nx,1),kf_Q);
+ nu = arma::mvnrnd(arma::zeros(ny,1),kf_R);
+
+ kf_y = kf_H*(kf_F*kf_x + eta) + nu;
+
+ measurement_function = new MeasurementModel(kf_H);
+ kf_cubature.update_sequential(kf_y,kf_x_pre,kf_P_x_pre,measurement_function,kf_R);
+
+ ckf_x_post = kf_cubature.get_x_est();
+ ckf_P_x_post = kf_cubature.get_P_x_est();
+
+ kf_P_y = kf_H * kf_P_x_pre * kf_H.t() + kf_R;
+ kf_K = (kf_P_x_pre * kf_H.t()) * arma::inv(kf_P_y);
+
+ kf_x_post = kf_x_pre + kf_K * (kf_y - kf_H * kf_x_pre);
+ kf_P_x_post = (arma::eye(nx,nx) - kf_K * kf_H) * kf_P_x_pre;
+
+ EXPECT_TRUE(arma::approx_equal(ckf_x_post, kf_x_post, "absdiff", 0.01));
+ EXPECT_TRUE(arma::approx_equal(ckf_P_x_post, kf_P_x_post, "absdiff", 0.01));
+
+ delete transition_function;
+ delete measurement_function;
+ }
+}