Gaussian mixture PHD filter for multi-sensor multi-target tracking with registration errors

  • Authors:

  • Wenling Li;Yingmin Jia;Junping Du;Fashan Yu

  • Affiliations:

  • The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China;The Seventh Research Division and the Department of Systems and Control, Beihang University (BUAA), Beijing 100191, China and Key Laboratory of Mathematics, Informatics and Behavioral Semantics (L ...;Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, Chi ...;School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, Henan, China

  • Venue:
  • Signal Processing
  • Year:
  • 2013

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Abstract

This paper studies the problem of multi-sensor multi-target tracking with registration errors in the formulation of random finite sets. The probability hypothesis density (PHD) recursion is applied by introducing the dynamics of the translational measurement bias into the associated intensity functions. Under the linear Gaussian assumptions on the bias dynamics, the Gaussian mixture implementation is used to give closed-form expressions. As the target state and the translational measurement bias are coupled through the likelihood in the update step, a two-stage Kalman filter is adopted to approximate the tractable form, which leads to a substantial reduction in computational complexity. Two numerical examples are provided to verify the effectiveness of the proposed filter.