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/****************************************************************************
*
* ViSP, open source Visual Servoing Platform software.
* Copyright (C) 2005 - 2023 by Inria. All rights reserved.
*
* This software 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 2 of the License, or
* (at your option) any later version.
* See the file LICENSE.txt at the root directory of this source
* distribution for additional information about the GNU GPL.
*
* For using ViSP with software that can not be combined with the GNU
* GPL, please contact Inria about acquiring a ViSP Professional
* Edition License.
*
* See https://visp.inria.fr for more information.
*
* This software was developed at:
* Inria Rennes - Bretagne Atlantique
* Campus Universitaire de Beaulieu
* 35042 Rennes Cedex
* France
*
* If you have questions regarding the use of this file, please contact
* Inria at visp@inria.fr
*
* This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
* WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
*
* Description:
* Tests some vpLinearKalmanFilterInstantiation functionalities.
*
*****************************************************************************/
/*!
\example testKalmanAcceleration.cpp
\brief Test some vpLinearKalmanFilterInstantiation functionalities
with constant acceleration state model.
*/
#include <fstream>
#include <iostream>
#include <visp3/core/vpLinearKalmanFilterInstantiation.h>
int main()
{
try {
unsigned int nsignal = 1; // Number of signal to filter
unsigned int niter = 100;
std::string filename = "/tmp/log.dat";
std::ofstream flog(filename.c_str());
vpLinearKalmanFilterInstantiation kalman;
vpLinearKalmanFilterInstantiation::vpStateModel model;
model = vpLinearKalmanFilterInstantiation::stateConstAccWithColoredNoise_MeasureVel;
kalman.setStateModel(model);
unsigned int size_state_vector = kalman.getStateSize() * nsignal;
unsigned int size_measure_vector = kalman.getMeasureSize() * nsignal;
vpColVector sigma_measure(size_measure_vector);
for (unsigned int signal = 0; signal < nsignal; signal++)
sigma_measure = 0.0001;
vpColVector sigma_state(size_state_vector);
for (unsigned int signal = 0; signal < nsignal; signal++) {
sigma_state[3 * signal] = 0.; // not used
sigma_state[3 * signal + 1] = 0.000001;
sigma_state[3 * signal + 2] = 0.000001;
}
vpColVector velocity_measure(size_measure_vector);
double rho = 0.9; // correlation
double dt = 0.2; // sampling period
for (unsigned int signal = 0; signal < nsignal; signal++)
velocity_measure[signal] = 3 + 2 * signal;
kalman.verbose(false);
kalman.initFilter(nsignal, sigma_state, sigma_measure, rho, dt);
for (unsigned int iter = 0; iter <= niter; iter++) {
std::cout << "-------- iter " << iter << " ------------" << std::endl;
for (unsigned int signal = 0; signal < nsignal; signal++) {
velocity_measure[signal] = 3 + 2 * signal + 0.3 * sin(vpMath::rad(360. / niter * iter));
}
std::cout << "measure : " << velocity_measure.t() << std::endl;
flog << velocity_measure.t();
// kalman.prediction();
kalman.filter(velocity_measure);
flog << kalman.Xest.t();
flog << kalman.Xpre.t();
std::cout << "Xest: " << kalman.Xest.t() << std::endl;
std::cout << "Xpre: " << kalman.Xpre.t() << std::endl;
flog << std::endl;
}
flog.close();
return EXIT_SUCCESS;
} catch (const vpException &e) {
std::cout << "Catch an exception: " << e << std::endl;
return EXIT_FAILURE;
}
}
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