Bayesian estimation of multi-object systems with independently identically distributed correlations

Jeremie Houssineau, Daniel E. Clark

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

Abstract

Recent generalisations of stochastic filtering methods to multi-object systems have become very popular for solving multi-target tracking problems over the last decade. However, there was previously no general means of introducing correlations between objects. In this article, we investigate generalisations of such multi-object filters for systems where there may be dependencies between objects. Determining probability and factorial moment densities is facilitated by the use of a recent result in variational calculus, a general form of Faà di Bruno's formula. The result is illustrated through the Probability Hypothesis Density (PHD) filter, as a first-order moment example of the general form.

Original languageEnglish
Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
PublisherIEEE
Pages228-231
Number of pages4
ISBN (Print)9781479949755
DOIs
Publication statusPublished - 2014
Event17th IEEE Workshop on Statistical Signal Processing 2014 - Gold Coast, Australia
Duration: 29 Jun 20142 Jul 2014

Conference

Conference17th IEEE Workshop on Statistical Signal Processing 2014
Abbreviated titleSSP 2014
CountryAustralia
CityGold Coast
Period29/06/142/07/14

Keywords

  • POINT-PROCESSES

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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  • Cite this

    Houssineau, J., & Clark, D. E. (2014). Bayesian estimation of multi-object systems with independently identically distributed correlations. In IEEE Workshop on Statistical Signal Processing Proceedings (pp. 228-231). [6884617] IEEE. https://doi.org/10.1109/SSP.2014.6884617