Adaptive Surface Ship-Wake Detection Based on Improved One-Class Support Vector Machine

被引：0
作者：
Wang, Cheng [1 ]
Yang, Tingfei [1 ]
Meng, Qiang [1 ]
机构：
[1] Northwestern Polytech Univ, Nav Inst, Xian, Peoples R China
来源：
PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS AND INDUSTRIAL INFORMATICS | 2015年 / 31卷
关键词：
mathematic wake echo signal model; One-class support vector machine; Sequential minimal optimization; ship-wake detector;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is difficult to collect bubble-wake signals from different ocean environments caused by various types of ship. One-class support vector machine (OCSVM) can make decision based on incomplete information. This paper found an OCSVM detection model which use only reflected signals without a bubble wake to detect the surface ship-wake. In order to improve the training efficiency, a training algorithm based on Sequential Minimal Optimization (SMO) was introduced for OCSVM. Grid search method and Particle Swarm Optimization (PSO) algorithm are used to search optimal parameter. The simulation shows that the proposed detector can detect the ship weak well and it was robust with respect to noisy signals.
引用
收藏
页码：656 / 661
页数：6
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