基于矩阵填充的二维自适应波束形成算法

发布时间：2018-07-04 13:30

本文选题：阵列信号处理 + 稀疏平面阵列　；参考：《航空学报》2016年05期

【摘要】：针对基于矩阵填充的二维自适应波束形成问题,提出一种基于奇异值门限(SVT)的特征分解线性约束最小方差(SVT-ELCMV)算法。首先建立二维自适应波束形成矩阵填充模型,其次验证接收信号矩阵满足零空间性质(NSP),并分析最小可恢复阵元数,最后以SVT算法将稀疏阵列信号恢复为完整信号,并通过修正的特征分解线性约束最小方差(LCMV)形成有效波束。算法解决了稀疏阵列平均副瓣大幅度上升的缺陷,且在平面阵列部分阵元无法正常工作时依然有效。计算机仿真表明:SVT-ELCMV算法可使稀疏阵列具有与完整阵列相同的二维波束形成能力,并可有效抑制干扰信号,验证了算法的有效性和优越性。
[Abstract]:Aiming at the two-dimensional adaptive beamforming problem based on matrix filling, a linear constrained minimum variance (SVT-ELCMV) algorithm based on singular value threshold (SVT) is proposed. Firstly, the filling model of two-dimensional adaptive beamforming matrix is established. Secondly, it is verified that the received signal matrix satisfies the null space property (NSP), and the minimum recoverable element number is analyzed. Finally, the sparse array signal is restored to the complete signal by SVT algorithm. An effective beam is formed by a modified eigenfactorization with linear constrained minimum variance (LCMV). The algorithm solves the problem that the average sidelobe of the sparse array increases greatly and is still effective when some elements of the planar array can not work properly. Computer simulation shows that the sparse array has the same two-dimensional beamforming capability as the complete array, and the interference signal can be suppressed effectively. The effectiveness and superiority of the algorithm are verified.
【作者单位】：南京理工大学电子工程与光电技术学院;
【基金】：国家自然科学基金(61401204)~~
【分类号】：TN911.7

【相似文献】