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Title: Kalman-gain aided particle PHD filter for multi-target tracking
Authors: Daniyan, Abdullah
Gong, Yu
Feng, Pengming
Chambers, Jonathon
Lambotharan, Sangarapillai
Keywords: Kalman-gain
Particle filter
Sequential Monte Carlo (SMC)
Probability hypothesis density (PHD) filter
Multitarget tracking (MTT)
Bayesian tracking
Issue Date: 2017
Publisher: IEEE
Citation: DANIYAN, A. ... et al, 2017. Kalman-gain aided particle PHD filter for multi-target tracking. IEEE Transactions on Aerospace and Electronic Systems, doi:10.1109/TAES.2017.2690530.
Abstract: We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error (MSE) 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.
Description: This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details if this licence are available at: http://creativecommons.org/licenses/by/3.0
Sponsor: 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.
Version: Published
DOI: 10.1109/TAES.2017.2690530
URI: https://dspace.lboro.ac.uk/2134/24704
Publisher Link: http://dx.doi.org/10.1109/TAES.2017.2690530
ISSN: 1557-9603
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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