A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

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A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking (2002)

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by M. Sanjeev Arulampalam , Simon Maskell , Neil Gordon

Venue:	IEEE TRANSACTIONS ON SIGNAL PROCESSING
Citations:	2002 - 2 self

BibTeX

@ARTICLE{Arulampalam02atutorial,
    author = {M. Sanjeev Arulampalam and Simon Maskell and Neil Gordon},
    title = {A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking},
    journal = {IEEE TRANSACTIONS ON SIGNAL PROCESSING},
    year = {2002},
    volume = {50},
    pages = {174--188}
}

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Abstract

Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

Keyphrases

particle filter online nonlinear non-gaussian bayesian tracking several variant rapid adaptation storage cost standard ekf signal characteristic point mass state-space model sequential importance sampling illustrative example generic framework many application area physical system nonlinear non-gaussian tracking problem traditional kalman probability density sequential monte carlo method suboptimal bayesian algorithm

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