Application of a Monte Carlo method for tracking maneuvering target in clutter

D.S. Angelova, Tz.A. Semerdjiev, V.P. Jilkov and E.A. Semerdjiev

Mathematics and Computers in Simulation (MATCOM), 2001, vol. 55, issue 1, 15-23

Abstract: The Monte Carlo methods provide a possibility for improved sub-optimal Bayesian estimation. In preceding studies the authors have suggested a new implementation of the general bootstrap simulation approach — the bootstrap multiple model (BMM) filter for tracking a maneuvering target. In the present paper this algorithm is further extended for operating in a cluttered environment. Probabilistic data association (PDA), taking into account the possible measurement-to-target association hypotheses, is incorporated into the BMM algorithm to overcome the measurement–origin uncertainty. By simulation the proposed BMM PDA algorithm is evaluated and compared with the well-known interacting multiple model (IMM) PDA filter. The obtained results demonstrate a superior tracking performance of the BMM PDA algorithm at the cost of an increase in computation.

Keywords: Multiple Model bootstrap filter; Monte Carlo methods; Tracking; Probabilistic data association (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:55:y:2001:i:1:p:15-23

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