Particle filtering with pairwise Markov processes

François Desbouvries; Wojciech Pieczynski

Particle filtering with pairwise Markov processes

CNRS SAMOVAR UMR 5157

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

The estimation of an unobservable process x from an observed process y is often performed in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the Kalman filter. On the other hand, particle filters are Monte Carlo based methods which provide approximate solutions in more complex situations. In this paper, we consider Pairwise Markov Models (PMM) by assuming that the pair (x, y) is Markovian. We show that this model is strictly more general than the HMM, and yet still enables particle filtering.

Original language	English
Pages (from-to)	705-708
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	6
Publication status	Published - 1 Jan 2003
Externally published	Yes
Event	2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong Duration: 6 Apr 2003 → 10 Apr 2003

Cite this

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AB - The estimation of an unobservable process x from an observed process y is often performed in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the Kalman filter. On the other hand, particle filters are Monte Carlo based methods which provide approximate solutions in more complex situations. In this paper, we consider Pairwise Markov Models (PMM) by assuming that the pair (x, y) is Markovian. We show that this model is strictly more general than the HMM, and yet still enables particle filtering.

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JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing

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ER -

Particle filtering with pairwise Markov processes

Abstract

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