PHD filtering with localised target number variance

Emmanuel D Delande, Jeremie Houssineau, Daniel E Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Mahler's Probability Hypothesis Density (PHD filter), proposed in 2000, addresses the challenges of the multiple-target detection and tracking problem by propagating a mean density of the targets in any region of the state space. However, when retrieving some local evidence on the target presence becomes a critical component of a larger process - e.g. for sensor management purposes - the local target number is insufficient unless some confidence on the estimation of the number of targets can be provided as well. In this paper, we propose a first implementation of a PHD filter that also includes an estimation of localised variance in the target number following each update step; we then illustrate the advantage of the PHD filter + variance on simulated data from a multiple-target scenario.

Original languageEnglish
Title of host publicationSignal Processing, Sensor Fusion, and Target Recognition XXII
EditorsIvan Kadar
PublisherSPIE
Number of pages13
Volume8745
DOIs
Publication statusPublished - 2013
EventSignal Processing, Sensor Fusion, and Target Recognition XXII - Baltimore, United States
Duration: 29 Apr 20132 May 2013

Publication series

NameProceedings of SPIE
Volume8745
ISSN (Print)1996-756X
ISSN (Electronic)0277-786X

Conference

ConferenceSignal Processing, Sensor Fusion, and Target Recognition XXII
CountryUnited States
CityBaltimore
Period29/04/132/05/13

Keywords

  • Multi-object filtering
  • PHD filter
  • Target number variance
  • Higher-order statistics

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