摘要
基于杂波谱稀疏恢复的空时自适应处理(STAP)方法可以显著降低对杂波样本数的要求,十分适合缺少样本情况下的机载雷达杂波抑制。然而,现有稀疏恢复STAP方法利用离散化空时导向矢量字典进行重构,在非正侧视阵情况下,由于杂波脊不在字典网格点上,字典失配问题严重影响杂波抑制性能。针对上述问题,该文提出了一种基于原子范数的无网格稀疏恢复空时自适应处理方法(ANM-STAP),利用低秩矩阵恢复理论实现连续空时平面的稀疏恢复,克服了稀疏恢复中的字典失配问题,获得了非正侧视阵情况下的高分辨率杂波空时谱,有效提高了STAP杂波抑制性能。Monte Carlo实验证明,该文方法STAP处理性能在非正侧视阵情况下优于已有字典离散化处理的稀疏恢复STAP方法。
Sparse recovery Space-Time Adaptive Processing(STAP)can reduce the requirements of clutter samples,and suppress effectively clutter using limited training samples for airborne radar.The whole spacetime plane is discretized into small grid points uniformly in presently available sparse recovery STAP methods,however,the clutter ridge is not located exactly on the pre-discretized grid points in non-sidelooking STAP radar.The dictionary mismatch effect degrades the performance of STAP significantly.In this paper,a gridless sparse recovery STAP method is proposed based on Atomic Norm Minimization(ANM-STAP),which utilizes the low-rank property of the clutter covariance matrix.In the proposed method,the clutter spectrum is precisely estimated in continuous space-time plane without dictionary mismatch.Numerical results show that the proposed method provides an improved performance to the sparse recovery STAP methods with discretized dictionaries.
作者
章涛
郭骏骋
来燃
ZHANG Tao;GUO Juncheng;LAI Ran(Tianjin Key Laboratory for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2021年第5期1235-1242,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(U1733116)
中央高校基本科研业务费中国民航大学资助专项(3122019048)
中国民航大学蓝天青年学者项目。
关键词
空时自适应处理
稀疏恢复
字典失配
原子范数
Space-Time Adaptive Processing(STAP)
Sparse recovery
Dictionary mismatch
Atomic norm
