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Game theoretic data association for multi-target tracking with varying number of targets

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conference contribution
posted on 27.03.2019, 09:58 by Abdullah Daniyan, Yu GongYu Gong, Sangarapillai LambotharanSangarapillai Lambotharan
We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets. The problem of target state-estimate-to-track data association has been considered. We use the SMC-PHD filter to handle the MTT aspect and obtain target state estimates. We model the interaction between target tracks as a game by considering them as players and the set of target state estimates as strategies. Utility functions for the players are defined and a regret-based learning algorithm with a forgetting factor is used to find the equilibrium of the game. Simulation results are presented to demonstrate the performance of the proposed technique.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and the MOD University Defence Research Collaboration (UDRC) in Signal Processing.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

2016 IEEE RADAR CONFERENCE (RADARCONF)

Pages

808 - 811

Citation

DANIYAN, A., GONG, Y. and LAMBOTHARAN, S., 2016. Game theoretic data association for multi-target tracking with varying number of targets. Presented at the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA, 2-6 May 2016.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2016

Notes

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781509008636

ISSN

2375-5318

Language

en