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      Multiple Maneuvering Targets Tracking Using MM-CBMeMBer Filter

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          Abstract

          The existing multiple model hypothesis density filter can estimate the number and state of maneuvering targets at the same time. Yet its Sequential Monte Carlo (SMC) implementation involves clustering algorithm, which is unstable and time consuming, and may result in tracking target loss. To solve the problem, this paper proposes a Multiple Model (MM) Cardinality Balanced Multiple target Multi-Bernoulli (CBMeMBer) filter. When the clutter number of per-scan is less than 20 and detection probability is higher than 0.9, this lgorithm transmits the posterior density of maneuvering targets through a set of time-varying Bernoulli parameters, according to which, the targets state can be computed by simple operations, thus effectively avoids the clustering algorithm. Simulation results shows that compared with multiple model hypothesis density filter, the algorithm proposed decreased the OSPA distance which chooses to estimate tracking errors.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 September 2012
          : 1
          : 3
          : 238-245
          Affiliations
          [1 ] University of Electronic Science and Technology of China
          Article
          1d2856c9cb9b401dbe201235e7c356ab
          10.3724/SP.J.1300.2012.20043

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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