摘要
基于稀疏恢复的空时自适应算法(Sparse Recovery Space Time Adaptive Processing,SR-STAP)能有效改善机载雷达在复杂环境下对杂波的抑制能力,通常是将空时平面均匀离散为若干个网格来构造字典。然而,真实的杂波点往往不能落在预先离散的网格点上,此时会出现离网效应,导致SR-STAP的性能降低。本文针对此问题,提出了一种基于知识辅助的字典校正方法。首先利用载机平台参数等先验知识均匀离散空间频率,然后计算和修正多普勒频率,并根据先验知识修正空间频率,最后利用修正后的空间频率和多普勒频率对应的空时导向矢量来构造超完备稀疏字典。仿真结果表明,与传统字典构造算法相比,该字典校正方法有效克服了离网效应,改善了STAP的性能。
The Sparse Recovery Space Time Adaptive Processing(SR-STAP)algorithm can effectively improve the clutter suppression performance of airborne radar in complex environment.Usually,the space-time plane is uniformly dispersed into several grids to construct the dictionary.However,the real clutter points often cannot fall on the pre-discrete grid points called off-grid,leading to the performance degradation of SR-STAP.To solve this problem,this paper proposes a knowledge-aided dictionary correction method.Firstly,a priori knowledge such as the parameters of the carrier platform is used to uniformly discretize the spatial frequencies.Then,the Doppler frequencies are calculated and corrected,and the spatial frequencies are corrected according to the prior knowledge.Finally,the space-time steering vectors corresponding to the revised spatial frequencies and Doppler frequencies are used to construct an overcomplete sparse dictionary.The simulation results show that,compared with the traditional dictionary construction algorithm,the proposed knowledge-aided dictionary correction method can effectively overcome the off-grid effect and improve the performance of STAP.
作者
张欢欢
高志奇
黄平平
徐伟
ZHANG Huanhuan;GAO Zhiqi;HUANG Pingping;XU Wei(College of Information Engineering,Inner Mongolia University of Technology,Hohhot,Inner Mongolia 010080,China;Inner Mongolia Key Laboratory of Radar Technology and Application,Hohhot,Inner Mongolia 010051,China)
出处
《信号处理》
CSCD
北大核心
2021年第7期1235-1242,共8页
Journal of Signal Processing
基金
国家自然科学基金项目(61761037)
内蒙古工业大学科学研究重点项目(ZD201717)
内蒙古自治区高等学校科学技术研究项目(NJZY19072)。
关键词
空时自适应
稀疏恢复
离网效应
知识辅助
space-time adaptive processing
sparse recovery
off-grid effect
knowledge aided
