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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。
To solve the problem of two-dimensional adaptive beamforming based on matrix packing, a feature decomposition linear constrained minimum variance (SVT-ELCMV) algorithm based on singular value threshold (SVT) is proposed. First, a two-dimensional adaptive beamforming matrix filling model is established. Secondly, the received signal matrix is verified to satisfy the zero-spatiality property (NSP), and the minimum recoverable array element number is analyzed. Finally, the sparse array signal is restored to a complete signal by the SVT algorithm. The characteristic decomposition linear constraint minimum variance (LCMV) forms an effective beam. The algorithm solves the problem that the average side lobe of a sparse array greatly increases, and is still effective when some array elements in a planar array can not work normally. Computer simulation shows that the SVT-ELCMV algorithm can make the sparse array have the same two-dimensional beamforming capability as the complete array, and can effectively suppress the interference signal, verifying the effectiveness and superiority of the algorithm.