Joint multi-target tracking and parameter estimation with the second-order factorial cumulant filter

Daniel E. Clark; Mark Campbell

Joint multi-target tracking and parameter estimation with the second-order factorial cumulant filter

Daniel E. Clark, Mark Campbell

School of Engineering & Physical Sciences

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

2 Citations (Scopus)

Abstract

This paper presents a low computational complexity solution to the problem of joint multi-target tracking and parameter estimation. The multi-target filtering approach is based on the linear-complexity factorial cumulant filter presented in this conference last year, and the parameter estimation approach is based on the single-cluster point process method for joint multi-object estimation and parameter estimation. The joint multitarget and measurement process is approximated at each step with a Panjer point process, which permits the low cost solution. It is shown in simulated studies the paper that the approach is more robust than using a Poisson point process prior since second-order information is retained and propagated.

Original language	English
Title of host publication	22nd International Conference on Information Fusion 2019
Publisher	IEEE
ISBN (Electronic)	9780996452786
Publication status	Published - 27 Feb 2020
Event	22nd International Conference on Information Fusion 2019 - Ottawa, Canada Duration: 2 Jul 2019 → 5 Jul 2019

Conference

Conference	22nd International Conference on Information Fusion 2019
Abbreviated title	FUSION 2019
Country/Territory	Canada
City	Ottawa
Period	2/07/19 → 5/07/19

Keywords

factorial cumulants
Multi-target tracking
point processes
single-cluster process

ASJC Scopus subject areas

Information Systems
Instrumentation

Cite this

@inproceedings{362856fcc5e54430ac31108636dc8d9a,

title = "Joint multi-target tracking and parameter estimation with the second-order factorial cumulant filter",

abstract = "This paper presents a low computational complexity solution to the problem of joint multi-target tracking and parameter estimation. The multi-target filtering approach is based on the linear-complexity factorial cumulant filter presented in this conference last year, and the parameter estimation approach is based on the single-cluster point process method for joint multi-object estimation and parameter estimation. The joint multitarget and measurement process is approximated at each step with a Panjer point process, which permits the low cost solution. It is shown in simulated studies the paper that the approach is more robust than using a Poisson point process prior since second-order information is retained and propagated.",

keywords = "factorial cumulants, Multi-target tracking, point processes, single-cluster process",

author = "Clark, {Daniel E.} and Mark Campbell",

note = "Funding Information: Daniel Clark is with Telecom SudParis, France. Email: [email protected] and Mark Camopbell is with Heriot-Watt University, UK. This work was supported by AFSOR grant FA9550-18-C-0201. Publisher Copyright: {\textcopyright} 2019 ISIF-International Society of Information Fusion.; 22nd International Conference on Information Fusion 2019, FUSION 2019 ; Conference date: 02-07-2019 Through 05-07-2019",

year = "2020",

month = feb,

day = "27",

language = "English",

booktitle = "22nd International Conference on Information Fusion 2019",

publisher = "IEEE",

address = "United States",

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TY - GEN

T1 - Joint multi-target tracking and parameter estimation with the second-order factorial cumulant filter

AU - Clark, Daniel E.

AU - Campbell, Mark

N1 - Funding Information: Daniel Clark is with Telecom SudParis, France. Email: [email protected] and Mark Camopbell is with Heriot-Watt University, UK. This work was supported by AFSOR grant FA9550-18-C-0201. Publisher Copyright: © 2019 ISIF-International Society of Information Fusion.

PY - 2020/2/27

Y1 - 2020/2/27

N2 - This paper presents a low computational complexity solution to the problem of joint multi-target tracking and parameter estimation. The multi-target filtering approach is based on the linear-complexity factorial cumulant filter presented in this conference last year, and the parameter estimation approach is based on the single-cluster point process method for joint multi-object estimation and parameter estimation. The joint multitarget and measurement process is approximated at each step with a Panjer point process, which permits the low cost solution. It is shown in simulated studies the paper that the approach is more robust than using a Poisson point process prior since second-order information is retained and propagated.

AB - This paper presents a low computational complexity solution to the problem of joint multi-target tracking and parameter estimation. The multi-target filtering approach is based on the linear-complexity factorial cumulant filter presented in this conference last year, and the parameter estimation approach is based on the single-cluster point process method for joint multi-object estimation and parameter estimation. The joint multitarget and measurement process is approximated at each step with a Panjer point process, which permits the low cost solution. It is shown in simulated studies the paper that the approach is more robust than using a Poisson point process prior since second-order information is retained and propagated.

KW - factorial cumulants

KW - Multi-target tracking

KW - point processes

KW - single-cluster process

UR - http://www.scopus.com/inward/record.url?scp=85080677101&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85080677101

BT - 22nd International Conference on Information Fusion 2019

PB - IEEE

T2 - 22nd International Conference on Information Fusion 2019

Y2 - 2 July 2019 through 5 July 2019

ER -

Joint multi-target tracking and parameter estimation with the second-order factorial cumulant filter

Abstract

Conference

Keywords

ASJC Scopus subject areas

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