## Abstract

Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure in a Hidden Markov Chain (HMC) model. In this paper we first shed some new light on two classical PF algorithms, which can be considered as natural MC implementations of two two-step direct recursive formulas for computing the filtering distribution. We next address the Particle Prediction (PP) problem, which happens to be simpler than the PF problem because the optimal prediction conditional importance distribution (CID) is much easier to sample from. Motivated by this result we finally develop two PP-based PF algorithms, and we compare our algorithms via simulations.

Original language | English (US) |
---|---|

Title of host publication | Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |

Pages | 303-308 |

Number of pages | 6 |

DOIs | |

State | Published - 2008 |

Externally published | Yes |

Event | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: Oct 16 2008 → Oct 19 2008 |

### Other

Other | 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 |
---|---|

Country/Territory | Mexico |

City | Cancun |

Period | 10/16/08 → 10/19/08 |

## Keywords

- Hidden Markov chains
- Optimal importance function
- Particle filtering
- Sampling importance resampling
- Sequential importance sampling

## ASJC Scopus subject areas

- Artificial Intelligence
- Software
- Electrical and Electronic Engineering