Kalman filtering using Pairwise Gaussian Models

Wojciech Pieczynski; François Desbouvries

Kalman filtering using Pairwise Gaussian Models

CNRS SAMOVAR UMR 5157

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

Abstract

An important problem in signal processing consists in recursively estimating an unobservable process x = {x_n}_n∈IN from an observed process y = {y_n}_n∈IN. This is done classically in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the well-known Kalman filter. In this paper, we consider Pairwise Gaussian Models by assuming that the pair (x, y) is Markovian and Gaussian. We show that this model is strictly more general than the HMM, and yet still enables Kalman-like filtering.

Original language	English
Pages (from-to)	57-60
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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AU - Pieczynski, Wojciech

AU - Desbouvries, François

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N2 - An important problem in signal processing consists in recursively estimating an unobservable process x = {xn}n∈IN from an observed process y = {yn}n∈IN. This is done classically in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the well-known Kalman filter. In this paper, we consider Pairwise Gaussian Models by assuming that the pair (x, y) is Markovian and Gaussian. We show that this model is strictly more general than the HMM, and yet still enables Kalman-like filtering.

AB - An important problem in signal processing consists in recursively estimating an unobservable process x = {xn}n∈IN from an observed process y = {yn}n∈IN. This is done classically in the framework of Hidden Markov Models (HMM). In the linear Gaussian case, the classical recursive solution is given by the well-known Kalman filter. In this paper, we consider Pairwise Gaussian Models by assuming that the pair (x, y) is Markovian and Gaussian. We show that this model is strictly more general than the HMM, and yet still enables Kalman-like filtering.

UR - https://www.scopus.com/pages/publications/0141854972

M3 - Conference article

AN - SCOPUS:0141854972

SN - 1520-6149

VL - 6

SP - 57

EP - 60

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

Y2 - 6 April 2003 through 10 April 2003

ER -

Kalman filtering using Pairwise Gaussian Models

Abstract

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