Data association using game theory for multi-target tracking in passive bistatic radar
| dc.contributor.author | Yu Gong | |
| dc.contributor.author | Abdullahi Daniyan | |
| dc.contributor.author | Abdulrazaq Aldowesh | |
| dc.contributor.author | Sangarapillai Lambotharan | |
| dc.date.accessioned | 2025-04-25T10:01:46Z | |
| dc.date.issued | 2017-06-20 | |
| dc.date.issued | 2017-05-01 | |
| dc.date.issued | 2019-03-27 | |
| dc.description.abstract | 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. | |
| dc.identifier | 10.1109/radar.2017.7944168 | |
| dc.identifier | 2625197920 | |
| dc.identifier.other | DOI: 10.1109/RADAR.2017.7944168 | |
| dc.identifier.uri | http://repository.futminna.edu.ng:4000/handle/123456789/984 | |
| dc.publisher | IEEE | |
| dc.source | UnpayWall | |
| dc.source | Crossref | |
| dc.source | Microsoft Academic Graph | |
| dc.subject | Game theory | |
| dc.subject | data association | |
| dc.subject | multi-target tracking | |
| dc.subject | passive bi-static radar PBR | |
| dc.subject | particle filter | |
| dc.subject | sequential Monte Carlo (SMC) | |
| dc.subject | PHD filter. | |
| dc.title | Data association using game theory for multi-target tracking in passive bistatic radar | |
| dc.type | Other |