particlefilter.h

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// $Id: particlefilter.h 29830 2009-01-14 15:10:41Z kgadeyne $

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#ifndef __PARTICLE_FILTER__
#define __PARTICLE_FILTER__

#include "filter.h"
#include "../pdf/conditionalpdf.h"
#include "../pdf/mcpdf.h"

// RS stands for Resample Scheme
// TODO: Work this better out
#define DEFAULT_RS MULTINOMIAL_RS // Default scheme = MULTINOMIAL
#define MULTINOMIAL_RS 0
/* Carpenter, Clifford and Fearnhead:
   Efficient implementation of particle
   filters for non-linear systems.

   See eg.

   @Article{        gordon93,
   author =      {Gordon, Neil and Salmond, D. J. and Smith, A. F. M.},
   title =       {Novel approach to nonlinear/non-Gaussian state estimation},
   journal =     {IEE Proceedings-F},
   year =        {1993},
   volume =      {140},
   number =      {2},
   pages =       {107--113},
   annote =      {Multinomial Sampling}}
*/

#define SYSTEMATIC_RS 1
// A lot of possible systematic approaches to resampling are described
// in literature.  The goal is to reduce the Monte Carlo Variance of
// Particle filters.  One example is
/*
@Article{        carpenter99-improved,
  author =       {Carpenter, J. and Clifford, P. and Fearnhead, P.},
  title =        {An {I}mproved {P}article {F}ilter for {N}on-linear {P}roblems},
  journal =      {Radar, Sonar and Navigation, IEE Proceedings -},
  year =         {1999},
  volume =       {146},
  number =       {1},
  pages =        {2--7},
  month =        {February},
  annote =       {Describes systematic resampling, variance reduction}
}
*/

// KG TODO Check if systematic resampling = deterministic resampling
// as described by kitagawa

#define STRATIFIED_RS 2
// Generate N samples not uniformly on [0,1] but generate 1 sample
// uniformly in for each "stratum" u_j ~ U(j-1/N,j/N)
// Variance reduction!
/*
  @Article{         kitagawa96,
  author =       {Kitagawa, G.},
  title =        {{M}onte {C}arlo filter and smoother for non-{G}aussian nonlinear state space models },
  journal =      {Journal of Computational and Graphical Statistics},
  year =         {1996},
  volume =       {5},
  number =       {1},
  pages =        {1--25},
  annote =       {describes deterministic and stratified resampling}
  }
*/

#define RESIDUAL_RS 3
// sample "deterministically" the integer part of N \times w_j , the
// perform multinomial sampling for the resulting N - \sum [N \times
// w_j] where [] denotes the integer part.
/*
  See eg. p.19
  @Article{         liuchen98,
  author =       {Liu, J. S. and Chen, R.},
  title =        {Sequential {M}onte {C}arlo methods for dynamic systems},
  journal =      {Journal of the American Statistical Association},
  year =         {1998},
  volume =       {93},
  pages =        {1032--1044}}
*/

#define MINIMUM_VARIANCE_RS 4
/*
  See eg.
  @TechReport{     carpenter99,
  author =       {Carpenter, J. and Clifford, P. and Fearnhead, P.},
  title =        {Building robust simulation-based filters for evolving data sets},
  institution =  {Department of Statistics, University of Oxford},
  year =         {99},
  note =         {\url{http://www.stats.ox.ac.uk/pub/clifford/Particle_Filters/}}}
*/

namespace BFL
{


  template <typename StateVar, typename MeasVar> class ParticleFilter
    : public Filter<StateVar,MeasVar>
    {
    protected:
      virtual bool UpdateInternal(SystemModel<StateVar>* const sysmodel,
                                  const StateVar& u,
                                  MeasurementModel<MeasVar,StateVar>* const measmodel,
                                  const MeasVar& z,
                                  const StateVar& s);


      ConditionalPdf<StateVar,StateVar> * _proposal;

      WeightedSample<StateVar>  _sample;
      vector<WeightedSample<StateVar> > _old_samples;
      vector<WeightedSample<StateVar> > _new_samples;
      vector<Sample<StateVar> > _new_samples_unweighted;
      typename vector<WeightedSample<StateVar> >::iterator _os_it;
      typename vector<WeightedSample<StateVar> >::iterator _ns_it;


      int _resamplePeriod;

      double _resampleThreshold;

      int _resampleScheme;

      bool _dynamicResampling;

      bool _proposal_depends_on_meas;

      bool _created_post;


      virtual bool ProposalStepInternal(SystemModel<StateVar> * const sysmodel,
                                        const StateVar & u,
                                        MeasurementModel<MeasVar,StateVar> * const measmodel,
                                        const MeasVar & z,
                                        const StateVar & s);


      virtual bool UpdateWeightsInternal(SystemModel<StateVar> * const sysmodel,
                                         const StateVar & u,
                                         MeasurementModel<MeasVar,StateVar> * const measmodel,
                                         const MeasVar & z,
                                         const StateVar & s);


      virtual bool DynamicResampleStep();


      virtual bool StaticResampleStep();

      virtual bool Resample();

    public:

      ParticleFilter(MCPdf<StateVar> * prior,
                     ConditionalPdf<StateVar,StateVar> * proposal,
                     int resampleperiod = 0,
                     double resamplethreshold = 0,
                     int resamplescheme = DEFAULT_RS);



      ParticleFilter(MCPdf<StateVar> * prior,
                     MCPdf<StateVar> * post,
                     ConditionalPdf<StateVar,StateVar> * proposal,
                     int resampleperiod = 0,
                     double resamplethreshold = 0,
                     int resamplescheme = DEFAULT_RS);

      virtual ~ParticleFilter();

      ParticleFilter(const ParticleFilter<StateVar,MeasVar> & filt);


      virtual void ProposalSet(ConditionalPdf<StateVar,StateVar>* const cpdf);


      ConditionalPdf<StateVar,StateVar> * ProposalGet();

      // implement virtual function
      virtual MCPdf<StateVar> * PostGet();
    };

#include "particlefilter.cpp"

} // End namespace BFL

#endif // __PARTICLE_FILTER__