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

Jeremie Houssineau; Daniel E. Clark

doi:10.1109/SSP.2014.6884617

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

Jeremie Houssineau^*, Daniel E. Clark

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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 language	English
Title of host publication	IEEE Workshop on Statistical Signal Processing Proceedings
Publisher	IEEE
Pages	228-231
Number of pages	4
ISBN (Print)	9781479949755
DOIs	https://doi.org/10.1109/SSP.2014.6884617
Publication status	Published - 2014
Event	17th IEEE Workshop on Statistical Signal Processing 2014 - Gold Coast, Australia Duration: 29 Jun 2014 → 2 Jul 2014

Conference

Conference	17th IEEE Workshop on Statistical Signal Processing 2014
Abbreviated title	SSP 2014
Country/Territory	Australia
City	Gold Coast
Period	29/06/14 → 2/07/14

Keywords

POINT-PROCESSES

ASJC Scopus subject areas

Electrical and Electronic Engineering
Applied Mathematics
Signal Processing
Computer Science Applications

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10.1109/SSP.2014.6884617

Cite this

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title = "Bayesian estimation of multi-object systems with independently identically distributed correlations",

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 Fa{\`a} 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.",

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author = "Jeremie Houssineau and Clark, {Daniel E.}",

note = "INSPEC Accession Number: 14544698; 17th IEEE Workshop on Statistical Signal Processing 2014, SSP 2014 ; Conference date: 29-06-2014 Through 02-07-2014",

year = "2014",

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AU - Houssineau, Jeremie

AU - Clark, Daniel E.

N1 - INSPEC Accession Number: 14544698

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - POINT-PROCESSES

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DO - 10.1109/SSP.2014.6884617

M3 - Conference contribution

AN - SCOPUS:84907418176

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EP - 231

BT - IEEE Workshop on Statistical Signal Processing Proceedings

PB - IEEE

T2 - 17th IEEE Workshop on Statistical Signal Processing 2014

Y2 - 29 June 2014 through 2 July 2014

ER -

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

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Cite this