School of Electrical Engineering and Technology (SEET)

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School of Electrical Engineering and Technology (SEET)

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Author: search.filters.author.Yu GongHas files: Yes

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    An improved resampling approach for particle filters in tracking
    (IEEE, 2017-11-06) Yu Gong; Sangarapillai Lambotharan; Abdullahi Daniyan
    Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement.
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    Data association using game theory for multi-target tracking in passive bistatic radar
    (IEEE, 2017-06-20) Yu Gong; Abdullahi Daniyan; Abdulrazaq Aldowesh; Sangarapillai 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.