School of Electrical Engineering and Technology (SEET)

Permanent URI for this communityhttp://197.211.34.35:4000/handle/123456789/24

School of Electrical Engineering and Technology (SEET)

Browse

Author: search.filters.author.Abdullahi DaniyanEnd date: 2019

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    Performance Analysis of Sequential Monte Carlo MCMC and PHD Filters on Multi-target Tracking in Video
    (IEEE, 2014-10-21) Abdullahi Daniyan
    The Bayesian approach to target tracking has proven to be successful in the tracking of multiple targets in various application contexts. This paper applies sequential Monte Carlo (SMC) filtering techniques such as the Markov Chain Monte Carlo particle filter (MCMC PF) and the SMC probability hypothesis density (PHD) filter as suboptimal Bayesian solutions to multi-target tracking (MTT) in video. The MCMC PF by virtue of its information-centric property, can automatically explore the posterior distribution at each sampling step making it possible to track multiple targets. In doing so, it propagates the full multi-target posterior. The SMC PHD filter however propagates only the first order moment of the multi-target posterior density thereby making it computationally less intensive. A comparison of both filters was carried out in tracking multiple human targets in a video scene demonstrating superior performance by the SMC PHD filter in a realistic scenario. The SMC PHD filter was seen to have higher performance than the MCMC PF in terms of the number of particles, the processing speed, and the tracking performance for multiple targets.