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
2017-12-06
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
velocity attributes , generalised Bayesian inference technique , radar blips , radar imaging , radar computing , moving vessels identification , Bayes methods , shape attributes , evidential reasoning rule , target recognition , likelihood information , coastal surveillance , inference mechanisms , ER rule , nonlinear optimisation model , radar images , image recognition , nonlinear programming , direction attributes
Metadata by IET is licensed under CC BY 3.0
Target recognition for coastal surveillance based on radar images and generalised Bayesian inference
Wiley | 2018
|PAPERS - Bayesian and Dempster -- Shafer Target Identification for Radar Surveillance
Online Contents | 2000
|Bayesian Gamma Mixture Model Approach to Radar Target Recognition
Online Contents | 2003
|