Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter

Tiancheng Li, Juan M. Corchado, Shudong Sun, Hongqi Fan

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

The ability to extract state-estimates for each target of a multi-target posterior, referred to as multi-estimate extraction (MEE), is an essential requirement for a multi-target filter, whose key performance assessments are based on accuracy, computational efficiency and reliability. The probability hypothesis density (PHD) filter, implemented by the sequential Monte Carlo approach, affords a computationally efficient solution to general multi-target filtering for a time-varying number of targets, but leaves no clue for optimal MEE. In this paper, new data association techniques are proposed to distinguish real measurements of targets from clutter, as well as to associate particles with measurements. The MEE problem is then formulated as a family of parallel single-estimate extraction problems, facilitating the use of the classic expected a posteriori (EAP) estimator, namely the multi-EAP (MEAP) estimator. The resulting MEAP estimator is free of iterative clustering computation, computes quickly and yields accurate and reliable estimates. Typical simulation scenarios are employed to demonstrate the superiority of the MEAP estimator over existing methods in terms of faster processing speed and better estimation accuracy.

Original languageEnglish
Pages (from-to)368-379
Number of pages12
JournalChinese Journal of Aeronautics
Volume30
Issue number1
DOIs
StatePublished - 1 Feb 2017
Externally publishedYes

Keywords

  • Data association
  • EAP estimator
  • Multi-target tracking
  • Particle filter
  • PHD filter

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