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基于小波网络的雷达目标识别方法

Radar Target Recognition Research based on Wavelet Neural Networ
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摘要 研究了小波网络的构造问题,提出了一种根据小波的局部时频特性,删除不包含任何目标识别样本数据的小波,初步确定网络隐层节点数的方法;采用本算法构造小波网络,可以减小网络的结构,加快网络的收敛速度。将构造好的小波网络作为雷达目标识别的分类器,获得了较好的识别效果。 The construction of the Wavelet Neural Network （WNN） is studied in this paper. Based on local time-frequency characteristics of wavelet, a method to primarily determine the number of nodes in hidden layer is proposed by deleting those wavelet which do not contain any sample data. WNN constructed 4 by the algorithm in this paper has smaller structure and faster convergence speed. Using the designed WNN as classifier of Radar Target Recognition, good recognition effect is achieved.
作者 朱志宇 张冰 刘维亭
机构地区 江苏科技大学电子信息学院
出处 《火力与指挥控制》 CSCD 北大核心 2007年第10期49-51,54,共4页 Fire Control & Command Control
基金 船舶工业国防科技应用 基础研究基金资助项目(01J3.17)
关键词 小波 神经网络 雷达 目标识别 wavelet, neural networks, radar, target recognition
分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TN959.17 [电子电信—信号与信息处理]
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参考文献6

  • 1张茁生,刘贵忠,张宗平.基于小波框架的自适应径向基函数网络[J].自动化学报,2002,28(2):229-237. 被引量：3
  • 2Leung H,Dubash N.Detection of Small in Clutter Using a GA-RBF Neural Network[J].IEEE Trans.On Aerospace and Electronic System.2002,38 (1):98-117.
  • 3Rong J,Ron T.Wavelet Neural Network Control of Induction Motor Drive Using Slide-mode Design Technique[J]IEEE Trans on Industrial Electronics.2003,50 (4):733-748.
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二级参考文献16

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火力与指挥控制
2007年 第10期

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