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Data association using game theory for multi-target tracking in passive bistatic radar

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conference contribution
posted on 2019-03-27, 11:15 authored by Abdullahi Daniyan, Abdulrazaq Aldowesh, Yu GongYu Gong, Sangarapillai LambotharanSangarapillai Lambotharan
We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets in a real passive bi-static radar (PBR) environment. The radar measurements were obtained through a PBR developed using National Instrument (NI) Universal Software Radio Peripheral (USRP). We considered the problem of associating target state-estimates-to-tracks for varying number of targets. We use the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter to perform the multi-target tracking in order to obtain the target state estimates and model the interaction between target tracks as a game. Experimental results using this real radar data demonstrate effectiveness of the game theoretic data association for multiple target tracking.

Funding

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1, the MOD University Defence Research Collaboration (UDRC) in Signal Processing, UK and the Petroleum Technology Development Fund (PTDF), Nigeria.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

2017 IEEE Radar Conference, RadarConf 2017

Pages

0042 - 0046

Citation

DANIYAN, A. ... et al, 2017. Data association using game theory for multi-target tracking in passive bistatic radar. Presented at the 2017 IEEE Radar Conference (RadarConf), Seattle, WA, USA, 8-12 May 2017, pp.0042-0046.

Publisher

© IEEE

Version

  • AM (Accepted Manuscript)

Publication date

2017

Notes

© 2017 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

9781467388238

eISSN

2375-5318

Language

  • en