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access icon free Robust target motion analysis using the possibility particle filter

© The Institution of Engineering and Technology
Received 04/05/2018, Accepted 09/08/2018, Revised 21/06/2018, Published 05/09/2018

Bearings-only target motion analysis (TMA) is the process of estimating the state of a moving emitting target from noisy measurements collected by a single passive observer. The focus of this study is on recursive TMA, traditionally solved using the Bayesian filters (e.g. extended or unscented Kalman filters, particle filters). The TMA is a difficult problem and may result in track divergence, especially when the assumed probabilistic models are imperfect or mismatched. As a robust alternative to Bayesian filters for TMA, the authors present a recently proposed stochastic filter referred to as the possibility filter. The filter is implemented in the sequential Monte Carlo framework, and named the possibility particle filter. This study demonstrates its superior performance against the standard (Bayesian) particle filter in the presence of a model mismatch, while in the case of the exact model match, its performance equals that of the standard particle filter.

Inspec keywords: Bayes methods; nonlinear filters; stochastic processes; particle filtering (numerical methods); Monte Carlo methods; Kalman filters; target tracking

Other keywords: moving emitting target; Bayesian filters; probabilistic models; bearings-only target motion analysis; robust target motion analysis; stochastic filter; possibility particle filter; standard particle filter; unscented Kalman filters; single passive observer; recursive TMA

Subjects: Monte Carlo methods; Signal processing theory; Monte Carlo methods; Filtering methods in signal processing

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