雷达辐射源分类方法

A radar emitter classification method based on the fusion ofinter-pulse features and intra-pulse features

摘要:

雷达辐射源个体识别技术是通过提取雷达细微特征判定载体身份属性,雷达辐射源信号个体识别的基础是在复杂电磁环境下将多路混叠接收信号进行正确的分选。针对传统基于脉间参数的信号分选容易造成脉冲分类不正确的问题,本文提出一种脉间参数结合脉内双谱特征的聚类算法,首先构建包含信号载频、脉冲宽度和脉冲围线积分形成的双谱波形熵的特征矩阵,然后采用改进自适应DBSCAN聚类算法对特征矩阵进行聚类分析。仿真结果表明,本文所提的算法通过真实数据采集验证,分类准确率在90%左右,能够满足后续个体识别需要。

Abstract:

The individual identification technology of radar emitter is to determine the identity attribute of the carrier by extracting the subtle features of the radar, and the basis of the individual identification of radar emitter signals is to correctly sort the multi-channel aliasing received signals in a complex electromagnetic environment. In order to solve the problem that the traditional signal sorting based on interpulse parameters is easy to cause incorrect pulse classification, this paper proposes a clustering algorithm based on interpulse parameters combined with intra-pulse bispectral features, which firstly constructs a feature matrix of bispectral waveform entropy composed of signal carrier frequency, pulse width and pulse enclosure integral, and then uses the improved adaptive DBSCAN clustering algorithm to cluster the feature matrix. The simulation results show that the proposed algorithm is verified by real data collection, and the classification accuracy is about 90%, which can meet the needs of subsequent individual identification.