vpLinearKalmanFilterInstantiation.cpp

/****************************************************************************
 *
 * This file is part of the ViSP software.
 * Copyright (C) 2005 - 2017 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://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:
 * Kalman filtering.
 *
 * Authors:
 * Eric Marchand
 * Fabien Spindler
 *
 *****************************************************************************/

#include <visp3/core/vpLinearKalmanFilterInstantiation.h>
#include <visp3/core/vpDebug.h>
#include <visp3/core/vpException.h>

#include <math.h>
#include <stdlib.h>

void
vpLinearKalmanFilterInstantiation::initFilter(unsigned int n_signal,
                                              vpColVector &sigma_state,
                                              vpColVector &sigma_measure,
                                              double rho, double delta_t)
{
  switch (model) {
  case stateConstVelWithColoredNoise_MeasureVel:
    initStateConstVelWithColoredNoise_MeasureVel(n_signal, sigma_state,
                         sigma_measure, rho);
    break;
  case stateConstVel_MeasurePos:
    initStateConstVel_MeasurePos(n_signal, sigma_state,
         sigma_measure, delta_t);
    break;
  case stateConstAccWithColoredNoise_MeasureVel:
    initStateConstAccWithColoredNoise_MeasureVel(n_signal, sigma_state,
             sigma_measure, rho, delta_t);
    break;
  case unknown:
    vpERROR_TRACE("Kalman state model is not set") ;    
    throw(vpException(vpException::notInitialized, "Kalman state model is not set")) ;
  }
}

void
vpLinearKalmanFilterInstantiation::initStateConstVel_MeasurePos(unsigned int n_signal,
                         vpColVector &sigma_state,
                         vpColVector &sigma_measure, 
               double delta_t )
{
  // init_done = true ;
  setStateModel(stateConstVel_MeasurePos);

  init(size_state, size_measure, n_signal);

  iter = 0;
  Pest = 0;
  Xest = 0;
  F    = 0;
  H    = 0;
  R    = 0;
  Q    = 0;
  this->dt = delta_t ;

  double dt2 = dt*dt ;
  double dt3 = dt2*dt ;

  for (unsigned int i=0;  i < size_measure*n_signal ;  i++ ) {
    // State model
    //         | 1  dt |
    //     F = |       |
    //         | 0   1 |

    F[2*i][2*i] = 1 ;
    F[2*i][2*i+1] = dt ;
    F[2*i+1][2*i+1] = 1 ;

    // Measure model
    H[i][2*i] = 1 ;
    H[i][2*i+1] = 0 ;

    double sR = sigma_measure[i] ;
    double sQ = sigma_state[2*i] ; // sigma_state[2*i+1] is not used 

    // Measure noise 
    R[i][i] = sR ;

    // State covariance matrix 6.2.2.12
    Q[2*i][2*i]     = sQ * dt3/3;
    Q[2*i][2*i+1]   = sQ * dt2/2;
    Q[2*i+1][2*i]   = sQ * dt2/2;
    Q[2*i+1][2*i+1] = sQ * dt;

    Pest[2*i][2*i]     = sR ;
    Pest[2*i][2*i+1]   = sR/(2*dt) ;
    Pest[2*i+1][2*i]   = sR/(2*dt) ;
    Pest[2*i+1][2*i+1] = sQ*2*dt/3.0+ sR/(2*dt2) ;
  }
}

void 
vpLinearKalmanFilterInstantiation::initStateConstVelWithColoredNoise_MeasureVel(unsigned int n_signal,
                           vpColVector &sigma_state,
                           vpColVector &sigma_measure,
                           double rho)
{
  if ((rho < 0) || (rho >= 1)) {
    vpERROR_TRACE("Bad rho value %g; should be in [0:1[", rho) ;    
    throw(vpException(vpException::badValue, "Bad rho value")) ;
  }

  setStateModel(stateConstVelWithColoredNoise_MeasureVel);

  init(size_state, size_measure, n_signal);

  iter = 0;
  Pest = 0;
  Xest = 0;
  F    = 0;
  H    = 0;
  R    = 0;
  Q    = 0;

  for (unsigned int i=0;  i < size_measure*n_signal ;  i++ ) {
    // State model
    //         | 1    1  |
    //     F = |         |
    //         | 0   rho |

    F[2*i][2*i] = 1 ;
    F[2*i][2*i+1] = 1 ;
    F[2*i+1][2*i+1] = rho ;

    // Measure model
    H[i][2*i] = 1 ;
    H[i][2*i+1] = 0 ;

    double sR = sigma_measure[i] ;
    double sQ = sigma_state[2*i+1] ; // sigma_state[2*i] is not used 

    // Measure noise 
    R[i][i] = sR ;

    // State covariance matrix
    Q[2*i][2*i] = 0 ;
    Q[2*i][2*i+1] = 0;
    Q[2*i+1][2*i] = 0;
    Q[2*i+1][2*i+1] = sQ  ;
 
    Pest[2*i][2*i]     = sR ;
    Pest[2*i][2*i+1]   = 0. ;
    Pest[2*i+1][2*i]   = 0 ;
    Pest[2*i+1][2*i+1] = sQ/(1-rho*rho) ;
  }
}

void
vpLinearKalmanFilterInstantiation::initStateConstAccWithColoredNoise_MeasureVel(unsigned int n_signal,
                             vpColVector &sigma_state,
                             vpColVector &sigma_measure,
                             double rho,
               double delta_t)
{
  if ((rho < 0) || (rho >= 1)) {
    vpERROR_TRACE("Bad rho value %g; should be in [0:1[", rho) ;    
    throw(vpException(vpException::badValue, "Bad rho value")) ;
  }
  setStateModel(stateConstAccWithColoredNoise_MeasureVel);

  init(size_state, size_measure, n_signal);

  iter = 0;
  Pest = 0;
  Xest = 0;
  F    = 0;
  H    = 0;
  R    = 0;
  Q    = 0;
  this->dt = delta_t;
  // initialise les matrices decrivant les modeles
  for (unsigned int i=0;  i < size_measure*nsignal ;  i++ ) {
    // State model
    //         | 1    1   dt |
    //     F = | o   rho   0 |
    //         | 0    0    1 |

    F[3*i][3*i] = 1 ;
    F[3*i][3*i+1] = 1 ;
    F[3*i][3*i+2] = dt ;
    F[3*i+1][3*i+1] = rho ;
    F[3*i+2][3*i+2] = 1 ;

    // Measure model
    H[i][3*i] = 1 ;
    H[i][3*i+1] = 0 ;
    H[i][3*i+2] = 0 ;

    double sR = sigma_measure[i] ;
    double sQ1 = sigma_state[3*i+1] ;
    double sQ2 = sigma_state[3*i+2] ;

    // Measure noise 
    R[i][i] = sR ;

    // State covariance matrix
    Q[3*i+1][3*i+1] = sQ1;
    Q[3*i+2][3*i+2] = sQ2;
 
    Pest[3*i][3*i]     = sR ;
    Pest[3*i][3*i+1]   = 0. ;
    Pest[3*i][3*i+2]   = sR/dt ;
    Pest[3*i+1][3*i+1] = sQ1/(1-rho*rho) ;
    Pest[3*i+1][3*i+2] = -rho*sQ1/((1-rho*rho)*dt) ;
    Pest[3*i+2][3*i+2] = (2*sR+sQ1/(1-rho*rho))/(dt*dt) ;
    // complete the lower triangle
    Pest[3*i+1][3*i]   = Pest[3*i][3*i+1];
    Pest[3*i+2][3*i]   = Pest[3*i][3*i+2];
    Pest[3*i+2][3*i+1] = Pest[3*i+1][3*i+2];
  }
}

void
vpLinearKalmanFilterInstantiation::filter(vpColVector &z)
{
  if (nsignal < 1) {
    vpERROR_TRACE("Bad signal number. You need to initialize the Kalman filter") ;    
    throw(vpException(vpException::notInitialized, 
              "Bad signal number")) ;
  }

  // Specific initialization of the filter that depends on the state model
  if (iter == 0) {
    Xest = 0;
    switch (model) {
    case stateConstVel_MeasurePos:
    case stateConstVelWithColoredNoise_MeasureVel:
    case stateConstAccWithColoredNoise_MeasureVel:
      for (unsigned int i=0;  i < size_measure*nsignal ;  i++ ) {
    Xest[size_state*i] = z[i] ;
      }
      prediction();
      //      init_done = true;
      break;
    case unknown:
      vpERROR_TRACE("Kalman state model is not set") ;    
      throw(vpException(vpException::notInitialized, "Kalman state model is not set")) ;
      break;
    }
    iter ++;

    return;
  }
  else if (iter == 1) {
    if (model == stateConstVel_MeasurePos) {
      for (unsigned int i=0;  i < size_measure*nsignal ;  i++ ) {
    double z_prev = Xest[size_state*i]; // Previous mesured position
    //  std::cout << "Mesure pre: " << z_prev << std::endl;
    Xest[size_state*i]   = z[i] ;
    Xest[size_state*i+1] = (z[i] - z_prev) / dt ;   
      }
      prediction();
      iter ++;
      
      return;
    }
  }

  filtering(z);
  prediction();

}