Open AccessAn Echo Sequence Profile Image Based Ship Target Classification Algorithm for Low-Resolution Radar
Author(s) -
Duoduo Hang,
Ji Zhang,
Chang Chen,
Wei Zhu,
Beibei Wu,
Zhang Hua,
Junbo Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2068/1/012011
Subject(s) - artificial intelligence , radar , support vector machine , computer science , algorithm , radar cross section , particle swarm optimization , computer vision , classifier (uml) , grayscale , pattern recognition (psychology) , echo (communications protocol) , image (mathematics) , telecommunications , computer network
Ship target classification is of great significance in both military and civilian fields. We propose a ship target classification algorithm for low-resolution radars with echo sequence profile images. This algorithm can be realized in the following steps. First, we collect radar profile image data. We use five perspectives of a radar target, including target shape, Radar Cross Section (RCS), echo amplitude, motion attribute, and features of two-dimensional grayscale maps, to extract eight-dimensional feature vectors. The proposed algorithm uses the Support Vector Machine (SVM) as the classifier, and the parameters of the classifier are optimized by either grid search or the Particle Swarm Optimization (PSO) algorithm. The proposed algorithm is verified through real data classification tests.