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Kalman-gain aided particle PHD filter for multi-target tracking
journal contributionposted on 2017-04-12, 10:25 authored by Abdullahi Daniyan, Yu GongYu Gong, Sangarapillai LambotharanSangarapillai Lambotharan, Pengming Feng, Jonathon Chambers
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.
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.
- Mechanical, Electrical and Manufacturing Engineering
Published inIEEE Transactions on Aerospace and Electronic Systems
CitationDANIYAN, A. ... et al, 2017. Kalman-gain aided particle PHD filter for multi-target tracking. IEEE Transactions on Aerospace and Electronic Systems, 53 (5), pp. 2251-2265.
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Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/
NotesThis 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