西南交通大学学报（英文版）2004，Vol.12Issue(2)：116-122,7.
Radar Emitter Signal Recognition Based on Complexity Features
Radar Emitter Signal Recognition Based on Complexity Features
摘要
Abstract
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.关键词
Signal processing/Lempel-Ziv complexity/Correlation dimension/Radar emitter signalsKey words
Signal processing/Lempel-Ziv complexity/Correlation dimension/Radar emitter signals分类
信息技术与安全科学引用本文复制引用
张葛祥,金炜东,胡来招..Radar Emitter Signal Recognition Based on Complexity Features[J].西南交通大学学报（英文版）,2004,12(2):116-122,7.基金项目
The National Defence Foundation (No.NEWL51435QT220401 ), the Doctoral Innovation Foundation of SWJTU, and the Main Teacher Sponsor Program of the Ministry of Education of China (No.65, 2000). （No.NEWL51435QT220401 ）
