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.
Target recognition for coastal surveillance based on radar images and generalised Bayesian inference
IET Intelligent Transport Systems ; 12 , 2 ; 103-112
2018-03-01
10 pages
Article (Journal)
Electronic Resource
English
nonlinear optimisation model , moving vessels identification , direction attributes , nonlinear programming , shape attributes , coastal surveillance , target recognition , evidential reasoning rule , likelihood information , Bayes methods , inference mechanisms , generalised Bayesian inference technique , ER rule , radar blips , radar computing , image recognition , radar images , radar imaging , velocity attributes
Target recognition for coastal surveillance based on radar images and generalised Bayesian inference
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