基于极大似然估计的非理想场景雷达信号分选算法  

Maximum likelihood estimation based deinterleaving algorithm ofradar signal in non-ideal scenarios

作　　者：陈柯宇 杨健 张伟[1,4] 孙国敏 邵怀宗 CHEN Keyu;YANG Jian;ZHANG Wei;SUN Guomin;SHAO Huaizong(School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Laboratory of Electromagnetic Space Cognition and Intelligent Control,Beijing 100089,China;School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100089,China;National Key Laboratory of Electromagnetic Space Security,Chengdu 610036,China)

机构地区：[1]电子科技大学信息与通信工程学院,四川成都611731 [2]电磁空间认知与智能控制技术实验室,北京100089 [3]北京理工大学网络空间安全学院,北京100089 [4]电磁空间安全全国重点实验室,四川成都610036

出　　处：《系统工程与电子技术》2024年第7期2276-2284,共9页Systems Engineering and Electronics

基　　金：国家自然科学基金(U20B2070)资助课题。

摘　　要：目前,已有研究针对理想条件下的雷达脉冲信号分选问题进行了详细阐述,但是缺乏杂散脉冲和缺失脉冲两种非理想情景下的模型表征。为解决这一问题,提出了一种基于极大似然估计的非理想场景雷达信号分选算法。该算法通过修正似然因子来表征杂散脉冲和缺失脉冲现象,提高在复杂场景下的分选准确率。当部分雷达先验信息已知时,所提算法模型具有更好的分选效果。仿真实验结果表明,与已有的极大似然模型和深度学习算法相比,所提算法在分选准确率上有显著提升,具有较高的应用价值。The existing research has elaborated on the problem of deinterleaving of radar pulse signal under ideal conditions,but lacks the model representation under two non-ideal scenarios,spurious pulses and lost pulses.To solve this problem,a deinterleaving algorithm of radar signals in non-ideal scenarios based on maximum likelihood estimation is proposed,which characterizes spurious pulse and lost pulse phenomena by modifying the likelihood factor,so as to improve the deinterleaving accuracy in complex scenarios.When part of the radar prior information is known,the proposed algorithm model has better deinterleaving effect.Simulation experiment results show that,compared with the existing maximum likelihood model and the deep learning algorithm,the proposed algorithm has a significant improvement in the deinterleaving accuracy and has high application value.

关 键 词：雷达信号分选 脉冲重复间隔 电子侦察 极大似然估计 

分 类 号：TN911.7[电子电信—通信与信息系统]