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A Linear Multisensor PHD Filter Using the Measurement Dimension Extension Approach

  • Weifeng Liu
  • Chenglin Wen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6729)

Abstract

The common probability hypothesis density (PHD) fiter is derived under the single sensor condition. The multisensor PHD (MPHD) filter is remarkably complex and thus is impractical to use. Mahler proposed a MPHD filter under the assumption of independence of all senors. This paper studies the linear multisensor-multitarget system. We propose a linear multisensor probability hypothesis density (LMPHD) filter. By combining measurement dimension extension (MDE) approach, we consider linear correlation of all sensors. A simulation is finally proposed to verify the effective of the L-MPHD filter.

Keywords

Target Tracking Probability Hypothesis Density Probability Hypothesis Density Filter Multitarget Tracking Track Maneuvering Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Weifeng Liu
    • 1
  • Chenglin Wen
    • 1
  1. 1.Hangzhou Dianzi UniversityHangzhouChina

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