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      A drift homotopy Monte Carlo approach to particle filtering for multi-target tracking

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          Abstract

          We present a novel approach for improving particle filters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is used to design a Markov Chain Monte Carlo step which is appended to the particle filter and aims to bring the particle filter samples closer to the observations. Also, we present a simple Metropolis Monte Carlo algorithm for tackling the target-observation association problem. We have used the proposed approach on the problem of multi-target tracking for both linear and nonlinear observation models. The numerical results show that the suggested approach can improve significantly the performance of a particle filter.

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          Most cited references 6

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          Sequential Monte Carlo Methods for Dynamic Systems

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            Sonar tracking of multiple targets using joint probabilistic data association

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              Sequential monte carlo methods for multi-target filtering with random finite sets

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                Author and article information

                Journal
                15 June 2010
                2011-02-09
                Article
                1006.3100

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                65C05, 65C30, 60G35, 62M20, 93E10
                Minor corrections, 27 pages, 8 figures
                math.NA

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