Small Target Detection in Sea Clutter Based on LS-SVM

被引：0

作者：

Ma, Huizhu ^{[1
]}

Li, Ying ^{[1
]}

机构：

[1] Harbin Engn Univ, Informat & Commun Engn Coll, Harbin, Peoples R China

来源：

2011 INTERNATIONAL CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND AUTOMATION (CCCA 2011), VOL I | 2010年

关键词：

sea clutter; target detection; support vector machine; particle swarm optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Because of sea clutter, small targets detection in sea has a certain difficulties. Many scholars try to solve these problems by neural network. However the defects of neural network restricted its development. Support vector machine shows its superiority in convergence process and the select of hidden nodes is avoided. This paper uses the particle swarm algorithm to optimize the least squares support vector machine, and overcome the experienced dependence when some important parameters are selected. The time needed for convergence is shortened effectively. This method is applied to the targets detection in sea, and the specific process model is given.

引用

页码：470 / 473

页数：4

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