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Proceedings Paper

Expected likelihood for tracking in clutter with particle filters
Author(s): Alan Marrs; Simon Maskell; Yaakov Bar-Shalom
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Paper Abstract

The standard approach to tracking a single target in clutter, using the Kalman filter or extended Kalman filter, is to gate the measurements using the predicted measurement covariance and then to update the predicted state using probabilistic data association. When tracking with a particle filter, an analog to the predicted measurement covariance is not directly available and could only be constructed as an approximation to the current particle cloud. A common alternative is to use a form of soft gating, based upon a Student's-t likelihood, that is motivated by the concept of score functions in classical statistical hypothesis testing. In this paper, we combine the score function and probabilistic data association approaches to develop a new method for tracking in clutter using a particle filter. This is done by deriving an expected likelihood from known measurement and clutter statistics. The performance of this new approach is assessed on a series of bearings-only tracking scenarios with uncertain sensor location and non-Gaussian clutter.

Paper Details

Date Published: 7 August 2002
PDF: 10 pages
Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002);
Show Author Affiliations
Alan Marrs, QinetiQ (United Kingdom)
Simon Maskell, QinetiQ (UK) and Cambridge Univ. (United Kingdom)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 4728:
Signal and Data Processing of Small Targets 2002
Oliver E. Drummond, Editor(s)

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