Extended object filtering using spatial independent cluster processes

Anthony Swain, Daniel Clark

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

38 Citations (Scopus)

Abstract

Recent research into multi-object filtering for non-standard targets introduced alternative approaches for target group representation. In these approaches a measurement model (likelihood) was suggested that led to a representation of the measurements as a spatial point process, namely a Poisson point process. In this paper we take a more traditional approach to extended target tracking. We assume a 'standard' measurement model (at most one measurement generated from a target point), but represent the target group (extended targets) as a spatial cluster process, in particular an independent cluster process with a fixed distribution on the component (daughter) process. With this assumption we are able to derive approximate measurement-update equations for the first order moment density of the extended object Bayes filter in a number of scenarios. Such approximations are Bayes optimal and provide estimates for the number of clusters (extended targets) and their locations.

Original languageEnglish
Title of host publication13th Conference on Information Fusion, Fusion 2010
Publication statusPublished - 2010
Event13th Conference on Information Fusion - Edinburgh, United Kingdom
Duration: 26 Jul 201029 Jul 2010

Conference

Conference13th Conference on Information Fusion
Abbreviated titleFusion 2010
CountryUnited Kingdom
CityEdinburgh
Period26/07/1029/07/10

Keywords

  • Estimation
  • Filtering
  • Spatial cluster processes
  • Tracking

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