BFL::ASIRFilter< StateVar, MeasVar > Class Template Reference

ASIR: Auxiliary Particle Filter. More...

`#include <asirfilter.h>`

Inheritance diagram for BFL::ASIRFilter< StateVar, MeasVar >:

## Public Member Functions | |

ASIRFilter (MCPdf< StateVar > *prior, int resampleperiod=0, double resamplethreshold=0, int resamplescheme=DEFAULT_RS) | |

Constructor. | |

virtual | ~ASIRFilter () |

Destructor. | |

## Protected Member Functions | |

virtual void | UpdateInternal (SystemModel< StateVar > *const sysmodel, const StateVar &u, MeasurementModel< MeasVar, StateVar > *const measmodel, const MeasVar &z, const StateVar &s) |

Actual implementation of updateinternal. |

class BFL::ASIRFilter< StateVar, MeasVar >

ASIR: Auxiliary Particle Filter.

This is a possible implementation of a particle filter, which in some/many cases will yield better results, since measures are taken to make the proposal density more similar to the posterior. See

@Article{ pitt_auxiliary, author = {Pitt, M. and Shephard, N.}, title = {Filtering via simulation: auxiliary particle filter}, journal = {Journal of the American Statistical Association}, year = {1999}, note = {forthcoming} }

for more details.

Note that this particular implementation:

- Uses currently a particular version of important sampling
- Still uses the the system pdf as "actual" proposal density, which amongs others means the results will be suboptimal in case of a log concave measurement density.

Particular issue with of proposalstep in case of ASIR filter... The current implementation uses the approximation (see p. 11 of the Pitt and Shephard paper, we use their notation here ---

denoting the state and

denoting the measurement)

where

is the *mean* value of the system pdf.

Note that the ASIR needs the measurementmodel for its proposalstep, to obtain a better proposal. Note also that normally, the ASIR performs better that the standard SIR filter in case of outliers, but worse for "normal data" (due to the extra resampling stage).

Definition at line 79 of file asirfilter.h.

template<typename StateVar , typename MeasVar >

BFL::ASIRFilter< SVar, MVar >::ASIRFilter | ( | MCPdf< StateVar > * | prior, |

int | resampleperiod = `0` , |
||

double | resamplethreshold = `0` , |
||

int | resamplescheme = `DEFAULT_RS` |
||

) |

Constructor.

**Precondition:**- you created the necessary models and the prior

**Parameters:**-
prior pointer to the Monte Carlo Pdf prior density resampleperiod fixed resampling period (if desired) resamplethreshold threshold used when dynamic resampling resamplescheme resampling scheme, see header file for different defines and their meaning

Definition at line 26 of file asirfilter.cpp.

template<typename SVar , typename MVar >

BFL::ASIRFilter< SVar, MVar >::~ASIRFilter | ( | ) | ` [virtual]` |

Destructor.

Definition at line 44 of file asirfilter.cpp.

template<typename StateVar , typename MeasVar >

void BFL::ASIRFilter< SVar, MVar >::UpdateInternal | ( | SystemModel< StateVar > *const | sysmodel, |

const StateVar & | u, |
||

MeasurementModel< MeasVar, StateVar > *const | measmodel, |
||

const MeasVar & | z, |
||

const StateVar & | s |
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) | ` [protected, virtual]` |

Actual implementation of updateinternal.

Reimplemented from BFL::ParticleFilter< StateVar, MeasVar >.

Definition at line 48 of file asirfilter.cpp.

The documentation for this class was generated from the following files:

bfl

Author(s): Klaas Gadeyne, Wim Meeussen, Tinne Delaet and many others. See web page for a full contributor list. ROS package maintained by Wim Meeussen.

autogenerated on Thu Feb 11 2016 22:31:57

Author(s): Klaas Gadeyne, Wim Meeussen, Tinne Delaet and many others. See web page for a full contributor list. ROS package maintained by Wim Meeussen.

autogenerated on Thu Feb 11 2016 22:31:57