Multi-target state estimation and track continuity for the particle PHD filter
Multi-target state estimation and track continuity for the particle PHD filter
Particle filter approaches for approximating the first-order moment of a joint, or probability hypothesis density (PHD), have demonstrated a feasible suboptimal method for tracking a time-varying number of targets in real-time. We consider two techniques for estimating the target states at each iteration, namely k-means clustering and mixture modelling via the expectation-maximization (EM) algorithm. We present novel techniques for associating the targets between frames to enable track continuity.
1441-1453
Clark, D.E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Bell, J.
1ae45580-f516-4e83-af09-b6a9aa63f0f7
October 2007
Clark, D.E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Bell, J.
1ae45580-f516-4e83-af09-b6a9aa63f0f7
Clark, D.E. and Bell, J.
(2007)
Multi-target state estimation and track continuity for the particle PHD filter.
IEEE Transactions on Aerospace and Electronic Systems, 43 (4), .
(doi:10.1109/TAES.2007.4441750).
Abstract
Particle filter approaches for approximating the first-order moment of a joint, or probability hypothesis density (PHD), have demonstrated a feasible suboptimal method for tracking a time-varying number of targets in real-time. We consider two techniques for estimating the target states at each iteration, namely k-means clustering and mixture modelling via the expectation-maximization (EM) algorithm. We present novel techniques for associating the targets between frames to enable track continuity.
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Published date: October 2007
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Local EPrints ID: 473677
URI: http://eprints.soton.ac.uk/id/eprint/473677
PURE UUID: 0c8b2975-df7b-45d9-97b7-846225bed055
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Date deposited: 27 Jan 2023 17:43
Last modified: 16 Mar 2024 23:15
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Author:
D.E. Clark
Author:
J. Bell
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