A Heavy-Tailed Noise Tolerant Labeled Multi-Bernoulli Filter

Wanying Zhang, Feng Yang, Yan Liang, Zhentao Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The well-known labeled multi-Bernoulli (LMB) filter for multi-target tracking in clutters works well only under the Gaussian noise assumptions. Since this Gaussian assumption can hardly hold in practice, we present the problem of the LMB with heavy-tailed non-Gaussian measurement noise. Through modeling the measurement noise as Student's t distribution, a heavy-tailed measurement noise tolerant LMB (TLMB) is derived in the framework of variational Bayesian inference for the joint estimation of the target state together with the unknown scale matrix and degree of freedom (dof) of the Student's t distribution. Simulations on multi-target tracking in clutter with unreliable sensor demonstrate the effectiveness and superiority of the proposed TLMB.

Original languageEnglish
Title of host publication2018 21st International Conference on Information Fusion, FUSION 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2461-2467
Number of pages7
ISBN (Print)9780996452762
DOIs
StatePublished - 5 Sep 2018
Event21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018

Publication series

Name2018 21st International Conference on Information Fusion, FUSION 2018

Conference

Conference21st International Conference on Information Fusion, FUSION 2018
Country/TerritoryUnited Kingdom
CityCambridge
Period10/07/1813/07/18

Keywords

  • heavy-tailed measurement noise
  • labeled multi-Bernoulli filter
  • multi-target tracking
  • S-tudent's distribution
  • variational Bayesian

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