Expected likelihood for tracking in clutter with particle filters

Alan Marrs; Simon Maskell; Yaakov Bar-Shalom

doi:10.1117/12.478507

7 August 2002 Expected likelihood for tracking in clutter with particle filters

Alan Marrs, Simon Maskell, Yaakov Bar-Shalom

Proceedings Volume 4728, Signal and Data Processing of Small Targets 2002; (2002) https://doi.org/10.1117/12.478507
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Alan Marrs, Simon Maskell, and Yaakov Bar-Shalom "Expected likelihood for tracking in clutter with particle filters", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002); https://doi.org/10.1117/12.478507

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