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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    Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking
    (IEEE, 2017-04-05) Abdullahi Daniyan; Yu Gong; Sangarapillai Lambotharan; Pengming Feng; Jonathon Chambers
    We propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.
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    Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines
    (IEEE, 2018-10-04) Abdullahi Daniyan; Sangarapillai Lambotharan; Anastasios Deligiannis; Yu Gong; Wen-Hua Chen
    In this paper, we propose a technique for the joint tracking and labeling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component, and target extension are defined and jointly propagated in time under the generalized labeled multi-Bernoulli filter framework. In particular, we developed a Poisson mixture variational Bayesian model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modeled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.