25 August 2004 Tracking maneuvering targets using a scale mixture of normals
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Abstract
The assumption of Gaussian noise in the system and measurement model has been standard practice for target tracking algorithm development for many years. For problems involving manoeuvring targets this is known to be an over-simplification and a potentially poor approximation. In this paper the use of heavy-tailed distributions is suggested as a means of efficiently modelling the behaviour of manoeuvring targets with a single dynamic model. We exploit the fact that all heavy-tailed distributions can be written as scale mixture of Normals to propose a Rao-Blackwellised particle filter (SMNPF) where particles sample the history of the continuous scale parameter and a Kalman filter is used to conduct the associated filtering for each particle. Schemes are proposed for making the proposal of new particles efficient. Performance of a heavy-tailed system model implemented via the SMNPF filter is compared against an IMM for a sample trajectory taken from a benchmark problem.
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Simon Maskell, Neil J. Gordon, Nick Everett, Martin Robinson, "Tracking maneuvering targets using a scale mixture of normals", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.542071; https://doi.org/10.1117/12.542071
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