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 of radar signal in non-ideal scenarios
陈柯宇 1杨健 2张伟 3孙国敏 4邵怀宗1
作者信息
- 1. 电子科技大学信息与通信工程学院,四川成都 611731;电磁空间认知与智能控制技术实验室,北京 100089
- 2. 电磁空间认知与智能控制技术实验室,北京 100089;北京理工大学网络空间安全学院,北京 100089
- 3. 电子科技大学信息与通信工程学院,四川成都 611731;电磁空间安全全国重点实验室,四川成都 610036
- 4. 电子科技大学信息与通信工程学院,四川成都 611731
- 折叠
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.
Abstract
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Key words
deinteleaving of radar signal/pulse repetition interval/electronic reconnaissance/maximum likelihood estimataion引用本文复制引用
出版年
2024
NETL
NSTL
万方数据