面向非协作多功能雷达的波形单元提取方法

Waveform unit extraction method for non-cooperative multi-function radar

针对当前波形单元提取技术难以应用于非协作多功能雷达(multi-function radar,MFR)实侦数据的问题,构建了基于MFR多参数序列的二分类模型,在此基础上提出了一种自适应确定输入参数的密度聚类算法进行分类。该方法无需依靠MFR波形库的先验知识,采用无监督学习的方式提取波形单元。同时,充分利用多参数间的联合变化和数据集的整体分布信息提高算法鲁棒性,并通过引入输入参数λ可对不同用户需求调整算法性能。仿真实验表明,该算法可以有效地提取非协作MFR波形单元,同时能够适应测量误差和脉冲丢失的干扰,具有良好的鲁棒性和准确性,有利于实际工程应用。

Aiming at the problems that the current waveform unit extraction technology is difficult to apply in the reconnaissance data of non-cooperative multi-function radar(MFR),a binary classification model based on MFR multi-parameter sequence is constructed,and a density clustering algorithm with adaptive input parameters for classification is proposed.This method does not need to rely on the prior knowledge of MFR waveform library,and uses unsupervised learning to extract waveform units.At the same time,making full use of the joint changes among multiple parameters and the overall distribution information of the data set to improve the robustness of the algorithm.The performance of the algorithm can be adjusted for different user requirements by setting the input parameterλ.Simulation results show that the proposed algorithm can effectively extract non-cooperative MFR waveform units,and can adapt to the interference caused by measurement error and pulse loss.It has good robustness and accuracy,and is conductive to practical engineering application.