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Target recognition for coastal surveillance based on radar images and generalised Bayesian inference

Ma, F, Chen, Y-W, Yan, X-P, Chu, X-M and Wang, J (2017) Target recognition for coastal surveillance based on radar images and generalised Bayesian inference. IET Intelligent Transport Systems, 12 (2). pp. 103-112. ISSN 1751-956X

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For coastal surveillance, this study proposes a novel approach to identify moving vessels from radar images with the use of a generalised Bayesian inference technique, namely the evidential reasoning (ER) rule. First of all, the likelihood information about radar blips is obtained in terms of the velocity, direction, and shape attributes of the verified samples. Then, it is transformed to be multiple pieces of evidence, which are formulated as generalised belief distributions representing the probabilistic relationships between the blip's states of authenticity and the values of its attributes. Subsequently, the ER rule is used to combine these pieces of evidence, taking into account their corresponding reliabilities and weights. Furthermore, based on different objectives and verified samples, weight coefficients can be trained with a non-linear optimisation model. Finally, two field tests of identifying moving vessels from radar images have been conducted to validate the effectiveness and flexibility of the proposed approach.

Item Type: Article
Uncontrolled Keywords: 0906 Electrical And Electronic Engineering
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: Institution of Engineering and Technology (IET)
Related URLs:
Date Deposited: 13 Mar 2018 09:42
Last Modified: 04 Sep 2021 02:54
DOI or ID number: 10.1049/iet-its.2017.0042
URI: https://researchonline.ljmu.ac.uk/id/eprint/8260
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