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Radar Emitter Signal Recognition Based on Complexity Features
被引：39
作者：
张葛祥
金炜东
胡来招
机构：
[1] SchoolofElectricalEngineering,SouthwestJiaotongUniversity,SchoolofElectricalEngineering,SouthwestJiaotongUniversity,NationalEWLaboratoryChengdu,China,NationalEWLaboratory,Chengdu,China,Chengdu,China,Chengdu,China
关键词：
D O I：
暂无
中图分类号：
TN957.51 [雷达信号检测处理];
学科分类号：
081002 [信号与信息处理];
摘要：
Intra-pulse characteristics of different radar emitter signals reflect on signal waveform by way of changing frequency, phase and amplitude. A novel approach was proposed to extract complexity features of radar emitter signals in a wide range of signal-to-noise ratio (SNR), and radial basis probability neural network (RBPNN) was used to recognize different radar emitter signals. Complexity features, including Lempel-Ziv complexity (LZC) and correlation dimension (CD), can measure the complexity and irregularity of signals, which mirrors the intra-pulse modulation laws of radar emitter signals. In an experiment, LZC and CD features of 10 typical radar emitter signals were extracted and RBPNN was applied to identify the 10 radar emitter signals. Simulation results show that the proposed approach is effective and has good application values because average accurate recognition rate is high when SNR varies in a wide range.
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页码：116 / 122
页数：7
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