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| Radar Emitter Signal Recognition Based on Complexity Features | |
| 作者姓名： | 张葛祥 金炜东 胡来招 |
| 作者单位： | [1]SchoolofElectricalEngineering,SouthwestJiaotongUniversity,Chengdu610031,China [2]NationalEWLaboratory,Chengdu610036,China |
| 基金项目： | TheNationalDefenceFoundation (No .NEWL5 14 35QT2 2 0 4 0 1) ,theDoctoralInnovationFoundationofSWJTU ,andtheMainTeacherSponsorProgramoftheMinistryofEducationofChina (No .6 5 ,2 0 0 0 ) |
| 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. |
| 关 键 词： | 信号处理 Lempel-Ziv复杂性 相关测定 雷达 发射信号 |
Radar Emitter Signal Recognition Based on Complexity Features |
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| Zhang Gexiang , Jin Weidong Hu Laizhao . School of Electrical Engineering,Southwest Jiaotong University,Chengdu ,China . National EW Laboratory,Chengdu ,China.Radar Emitter Signal Recognition Based on Complexity Features[J].Journal of Southwest Jiaotong University,2004,12(2):116-122. | |
| Authors: | Zhang Gexiang Jin Weidong Hu Laizhao School of Electrical Engineering Southwest Jiaotong University Chengdu China National EW Laboratory Chengdu China |
| Institution: | 1. School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China;National EW Laboratory,Chengdu 610036,China 2. School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China 3. National EW Laboratory,Chengdu 610036,China |
| 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. |
| Keywords: | Signal processing Lempel-Ziv complexity Correlation dimension Radar emitter signals |
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