首页> 外文期刊>Acta Physica Polonica >Automatic Classification of LFM Signals for Radar Emitter Recognition Using Wavelet Decomposition and LVQ Classifier
【24h】

Automatic Classification of LFM Signals for Radar Emitter Recognition Using Wavelet Decomposition and LVQ Classifier

机译:基于小波分解和LVQ分类器的LFM信号自动分类用于雷达辐射源识别

获取原文
获取原文并翻译 | 示例
           

摘要

The paper presents a novel approach, based on the wavelet decomposition and the learning vector quantisation algorithm, to automatic classification of signals with linear frequency modulation, generated by radar emitters. The goal of radar transmitter classification is to determine the particular transmitter, from which a signal originated, using only the just received waveform. To categorise a current linear frequency modulation signal to the particular transmitter, the discrete wavelet decomposition of the received signal is accomplished in order to get a representative set of features with good classification properties. The learning vector quantisation algorithm with a previously defined set of features as an input of the learning vector quantisation neural net is proposed as the intelligent classification algorithm, which combines competitive learning with supervision. After the learning process, the learning vector quantisation algorithm is ready to perform the classification process for different data than data used in the learning stage. Simulation results show the high classification accuracy for experimentally chosen wavelets and suggested architecture of the learning vector quantisation classifier.
机译:本文提出了一种基于小波分解和学习矢量量化算法的新方法,该方法可以自动分类雷达发射器产生的具有线性调频的信号。雷达发射机分类的目的是仅使用刚刚接收到的波形来确定信号所源自的特定发射机。为了将当前线性频率调制信号分类到特定的发射机,完成接收信号的离散小波分解以便获得具有良好分类特性的代表性特征集。将具有预先定义的特征集作为学习矢量量化神经网络输入的学习矢量量化算法作为智能分类算法提出,该算法将竞争性学习与监督结合在一起。在学习过程之后,学习矢量量化算法已准备好对不同于学习阶段中使用的数据的数据执行分类过程。仿真结果表明,通过实验选择的小波具有很高的分类精度,并提供了学习矢量量化分类器的建议架构。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号