Distributed mean-field-type filter for vehicle tracking

Jian Gao, Hamidou Tembine

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

Abstract

Particle filter is an effective tool for vehicle tracking. However, we need to maintain a large number of particles to keep a reasonable tracking accuracy for multi-target tracking in large scale state space. This paper proposes a new distributed mean-field-type filter to handle those noisy, partial-observed and high-dimensional data. The state space is decomposed and the particles are deployed locally and updated independently in the simplified subspaces. The filtering framework contains four operations: sampling, prediction, decomposition and correction. A mean-field term is included in the system dynamic so that the prediction is based on the previous state as well as its statistic distribution, which is estimated by a multi-frame learning procedure. The experiment on real data shows that our approach can achieve accurate tracking results with a small number of particles.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4454-4459
Number of pages6
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2017 American Control Conference, ACC 2017
CountryUnited States
CitySeattle
Period5/24/175/26/17

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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