基于随机森林的雷达辐射源识别算法研究

A Radar Emitter Identification Algorithm Based on Random Forest

为解决低信噪比条件下传统雷达辐射源识别准确性低、时效性差、稳健性不强的问题,提出了一种基于随机森林的雷达辐射源型号识别算法。算法以载频、脉宽、脉冲重复周期为识别特征向量,首先从先验样本集中随机抽取得到多个训练集,然后使用训练集构建多个决策树分类器,最后通过多个决策树分类器对新识别特征向量进行识别并投票得到最终识别结果。仿真实验表明,该算法在低信噪比条件下依然具有较好的稳健性与时效性,能够有效解决战场雷达辐射源识别的问题。

Traditional radar emitter identification suffers from low accuracy,bad real-time performance and low robustness under the condition of low SNR. To solve the problem,a radar emitter identification algorithm based on random forest is proposed. This algorithm takes Carrier Frequency(CF),Pulse Width(PW) and Pulse Recurrence Interval(PRI) as features of identification. Firstly,random sampling is conducted on the priori sample set to obtain multiple training sets. Secondly,the training sets are used to build a number of decision tree classifiers. Finally,the decision tree classifiers are used to identify new features and the final identification results are obtained by voting. Simulation results show that this algorithm has good robustness and real-time performance even under low SNR,which can effectively identify radar emitter on the battlefield.