基于时频分布Rényi熵特征的雷达辐射源识别
Radar emitter recognition based on rényi entropy of time-frequency distribution
白航 1赵拥军 2沈伟 2徐永刚3
作者信息
- 1. 解放军信息工程大学信息工程学院,河南郑州450002;61906部队,河北廊坊065001
- 2. 解放军信息工程大学信息工程学院,河南郑州450002
- 3. 61906部队,河北廊坊065001
- 折叠
摘要
针对复杂体制雷达辐射源识别,提出一种基于时频分布Rényi熵的雷达信号特征提取和识别方法.该方法首先对雷达辐射源信号进行时频变换,然后提取信号时频分布的3阶、7阶和11阶Rényi熵作为特征向量,得到具有维数低、类间差异较大的识别特征.最后采用支持向量机分类器实现信号的分类识别.文中对8种常见雷达信号进行了仿真实验,结果表明在较大的信噪比范围内,该方法能获得较为满意的正确识别率,当信噪比为-3dB时,采用时频分布Rényi熵特征的平均识别率仍能达到90.75％,验证了提出方法的有效性.
Abstract
To correctly classify advanced radar emitter signals,a novel approach adopting Rényi Entropy of time frequency distribution for radar emitter signal recognition is proposed.Time-frequency distribution of radar emitter signals are obtained by using time-frequency reassignment transform,and then the third-order,seventh-order and eleventh-order Rényi Entropy of time-frequency distribution are used to construct a feature vector which has low dimensions and large between-class difference for radar signal recognition.Finally the support vector machine is used to identify radar emitter signals automatically.Simulation results show that the proposed approach can achieve satisfying accurate recognition over a wide range of SNR scenarios.Even for SNR=-3dB,the accurate recognition rate still achieves 90.75％ by using Rényi Entropy of time-frequency distribution.The validity of the approach is demonstrated by experiments.
关键词
Rényi熵/时频重排/支持向量机/雷达辐射源识别Key words
rényi entropy/time-frequency reassignment/support vector machines/radar emitter recognition引用本文复制引用
出版年
2013
NETL
NSTL
维普
万方数据