基于多PRI脉压回波的单通道雷达干扰分离方法
Interference separation method of single-channel radar based on multi-PRI pulse compression echoes
刘梅 1杨威 1高勋章 1刘永祥1
作者信息
- 1. 国防科技大学电子科学学院,湖南长沙 410073
- 折叠
摘要
研究了一种基于盲源分离的单通道雷达有源相干干扰抑制方法.针对伴随式干扰场景,在间歇采样转发干扰、部分脉冲密集转发干扰等有源相干干扰并存的条件下,建立了基于脉冲压缩的多脉冲重复间隔采样抗主瓣有源相干干扰模型并对其可行性进行了证明.针对现有单通道盲源分离方法在时域进行分离效果有限的问题,提出对脉压后的距离维数据进行盲源分离的思想,可在一定程度上抑制噪声对盲源分离的影响.同时,由于目标信号和干扰信号在脉冲压缩后重合度较低,可获得更好的分离效果.仿真和实测结果表明,在多个干扰源存在的条件下,本方法可有效分离真实 目标回波和干扰回波.当信噪比大于5 dB时,目标脉压回波相似系数接近100％;选取虚拟通道数越多,分离效果越好.
Abstract
This paper investigated a single-channel radar coherent interference suppression method based on blind source separation.For companion interference scenarios,under the co-existence of active coherent interference(such as interrupted sampling and repeater jamming and partial dense false target interference),an anti-main-lobe active interference model based on multiple pulse compression was established with feasibility demonstration.Considering that most existing single-channel blind source separation methods are operated in the time do-main with limited separation effectiveness,the idea of utilizing distance-domain pulse com-pression data for blind source separation was proposed,which can suppress the influence of noise on blind source separation to a certain extent.At the same time,the target signal and the interfering signal have a low degree of coincidence in the distance dimension,which can achieve a better separation performance.The simulation and measured data results have shown that the proposed method has a significant suppression performance against multiple active interferences,and when SNR is greater than 5 dB,the target pulse pressure echo simi-larity coefficient is close to 100％.Moreover,the increase of virtual channels would improve the performance of the method.
关键词
单通道盲分离/多PRI采样/抗主瓣有源干扰/最小描述长度准则Key words
single-channel blind separation/multi-PRI sampling/anti-main lobe active inter-ference/mininum description length(MDL)principle引用本文复制引用
基金项目
国家自然科学基金资助项目(61871384)
国家自然科学基金资助项目(61901487)
国家自然科学基金资助项目(61901498)
国家自然科学基金资助项目(61921001)
出版年
2024
NETL
NSTL
维普
万方数据