Application of a Monte Carlo method for tracking maneuvering target in clutter
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. (C) 2001 IMACS. Published by Elsevier Science B.V. All rights reserved.
monte carlo methods tracking probabilistic data association multiple model bootstrap filter