testKalmanAcceleration.cpp

Test some vpLinearKalmanFilterInstantiation functionalities with constant acceleration state model.

/****************************************************************************
 *
 * $Id: testKalmanAcceleration.cpp 4574 2014-01-09 08:48:51Z fspindle $
 *
 * This file is part of the ViSP software.
 * Copyright (C) 2005 - 2014 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
 * ("GPL") version 2 as published by the Free Software Foundation.
 * 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 http://www.irisa.fr/lagadic/visp/visp.html for more information.
 * 
 * This software was developed at:
 * INRIA Rennes - Bretagne Atlantique
 * Campus Universitaire de Beaulieu
 * 35042 Rennes Cedex
 * France
 * http://www.irisa.fr/lagadic
 *
 * 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.
 *
 * Authors:
 * Fabien Spindler
 *
 *****************************************************************************/

#include <visp/vpLinearKalmanFilterInstantiation.h>
#include <iostream>
#include <fstream>

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 0;
  }
  catch(vpException e) {
    std::cout << "Catch an exception: " << e << std::endl;
    return 0;
  }
}