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      An Improved SMC-PHD Filter for Multiple Targets Tracking

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          Abstract

          Two improved contributions have been advanced for the standard Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter. Firstly, a novel method is advanced for the cardinality and state estimation. A weight matrix is firstly calculated by measurements and persistent particles, and the weight sum of each row is then evaluated, the measurements indexed by row will be judged as true if its weight sum is larger than a certain threshold, and the weight sum of persistent particle states will be reported as the true target states. Secondly, an assistant variable which is used to denote the persistent age for every particle is introduced, by the help of this age variable, the overrated problem of targets number in dense clutter environment can be effectively restricted. The results of numerical simulation prove that the improved SMC-PHD filter has higher tracking performance than the standard one.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 December 2012
          : 1
          : 4
          : 406-413
          Affiliations
          [1 ] Electronic and Engineering Institute
          Article
          50d4758196314682b0a5a774da659486
          10.3724/SP.J.1300.2012.20094

          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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