A Robust Student's t-Based Labeled Multi-Bernoulli Filter

Wanying Zhang; Yan Liang; Feng Yang; Linfeng Xu

A Robust Student's t-Based Labeled Multi-Bernoulli Filter

Wanying Zhang
, Yan Liang
, Feng Yang
, Linfeng Xu

Ministry of Education of the People's Republic of China

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

5 Scopus citations

Abstract

The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Student's t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Student's t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Student's t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.

Original language	English
Title of host publication	FUSION 2019 - 22nd International Conference on Information Fusion
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780996452786
State	Published - Jul 2019
Externally published	Yes
Event	22nd International Conference on Information Fusion, FUSION 2019 - Ottawa, Canada Duration: 2 Jul 2019 → 5 Jul 2019

Publication series

Name	FUSION 2019 - 22nd International Conference on Information Fusion

Conference

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

Keywords

Student's t distribution
heavy-tailed noises
labeled multi-Bernoulli filter
multi-target tracking
variational Bayesian

Cite this

@inproceedings{74e1df56cbb94a6eb586298be43d82ce,

title = "A Robust Student's t-Based Labeled Multi-Bernoulli Filter",

abstract = "The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Student's t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Student's t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Student's t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.",

keywords = "Student's t distribution, heavy-tailed noises, labeled multi-Bernoulli filter, multi-target tracking, variational Bayesian",

author = "Wanying Zhang and Yan Liang and Feng Yang and Linfeng Xu",

note = "Publisher Copyright: {\textcopyright} 2019 ISIF-International Society of Information Fusion.; 22nd International Conference on Information Fusion, FUSION 2019 ; Conference date: 02-07-2019 Through 05-07-2019",

year = "2019",

month = jul,

language = "英语",

series = "FUSION 2019 - 22nd International Conference on Information Fusion",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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

}

Zhang, W, Liang, Y, Yang, F & Xu, L 2019, A Robust Student's t-Based Labeled Multi-Bernoulli Filter. in FUSION 2019 - 22nd International Conference on Information Fusion., 9011260, FUSION 2019 - 22nd International Conference on Information Fusion, Institute of Electrical and Electronics Engineers Inc., 22nd International Conference on Information Fusion, FUSION 2019, Ottawa, Canada, 2/07/19.

A Robust Student's t-Based Labeled Multi-Bernoulli Filter. / Zhang, Wanying; Liang, Yan; Yang, Feng et al.
FUSION 2019 - 22nd International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc., 2019. 9011260 (FUSION 2019 - 22nd International Conference on Information Fusion).

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

TY - GEN

T1 - A Robust Student's t-Based Labeled Multi-Bernoulli Filter

AU - Zhang, Wanying

AU - Liang, Yan

AU - Yang, Feng

AU - Xu, Linfeng

PY - 2019/7

Y1 - 2019/7

N2 - The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Student's t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Student's t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Student's t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.

AB - The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Student's t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Student's t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Student's t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.

KW - Student's t distribution

KW - heavy-tailed noises

KW - labeled multi-Bernoulli filter

KW - multi-target tracking

KW - variational Bayesian

UR - https://www.scopus.com/pages/publications/85081788737

M3 - 会议稿件

AN - SCOPUS:85081788737

T3 - FUSION 2019 - 22nd International Conference on Information Fusion

BT - FUSION 2019 - 22nd International Conference on Information Fusion

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Conference on Information Fusion, FUSION 2019

Y2 - 2 July 2019 through 5 July 2019

ER -

A Robust Student's t-Based Labeled Multi-Bernoulli Filter

Abstract

Publication series

Conference

Keywords

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