支持向量机在末制导雷达抗干扰中的应用

Application of the Support Vector Machine in the Terminal Guidance Radar of Anti-jamming

针对主被动末制导雷达的多维特征数据融合问题,提出采用支持向量机进行融合识别,以排除各类干扰形成的虚假目标。在定性分析末制导雷达各类干扰源特性的基础上,选取主动雷达目标RCS、距离维尺度、多普勒谱宽和被动雷达在该方位的特征信号检测结果作为参与融合的特征,利用线性支持向量机进行融合。仿真结果表明,仅使用主动雷达特征时,真假目标测试样本识别率为74%,使用被动雷达特征后,测试样本识别率提高到83%。

In order to eliminate the decoy targets caused by various jamming,the support vector machine adopted in the target fusion recognition is presented to solve the multi-dimension feature data fusion problem in active-passive composite terminal guidance radar.Through a quantitative analysis of the feature of jamming aimed at the terminal guidance radar,the parameters including the RCS,the scale of range dimension,Doppler width of the active radar,and the output of the passive radar detection with feature signal are selected,and then fused adopting the support vector machine.The simulation result shows that only using the active radar feature,the recognition ratio of the true-false targets testing sample is 74%.The recognition ratio is increased to 83% by using the active and passive radar feature simultaneously.