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      Improved Best-fitting Gaussian Approximation PHD Filter

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          Abstract

          The best-fitting Gaussian approximation Probability Hypothesis Density (PHD) filter is a novel algorithm for multiple maneuvering target tracking. However, there is a problem that the model probabilities are calculated without the measurement innovation. To solve this problem, an improved algorithm is proposed in this paper, which develops an update procedure for model probabilities to employ the posterior measurement innovation to enhance the filtering performance. Then, the dynamic equations can be softly switched among different models according to the likelihood functions. The simulation results show that the improved algorithm has the advantages over the ordinary one in the aspects of target number estimation and filtering accuracy, implying good application prospect.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 June 2013
          : 2
          : 2
          : 239-246
          Affiliations
          [1 ] Science and Technology on Electronic Information Control Laboratory
          Article
          a5fd53685b394b0bbeb26a6afb51686d
          10.3724/SP.J.1300.2013.13010

          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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