基于SVD和罚函数约束的子阵级方向图控制

Subarray Level Pattern Control Based on Singular Value Decomposition and Penalty Function Constraint

对于子阵级自适应波束形成中旁瓣电平较高的问题,提出一种基于奇异值分解和罚函数约束(SVDPFC)的子阵级自适应方向图控制算法。该算法通过对输入数据进行奇异值分解,得到干扰子空间和噪声子空间,通过干扰子空间修正约束矩阵和约束响应矢量,可在小快拍情况下有效抑制干扰,同时结合罚函数对自适应方向图约束,使其逼近期望静态方向图。该算法具有良好的副瓣特性,提高了小快拍情况下输出的信干噪比,仿真实验证明了该算法的可行性和有效性。

To solve the problem of high sidelobe in adaptive beamforming at subarray level,an adaptive pattern control algorithm based on singular value decomposition and penalty function constraints（ SVD-PFC） at subarray level is proposed. The algorithm obtains the interference subspace and noise subspace by the singular value decomposition of the input data,the constraint matrix and the constraint response vector are modified by the interference subspace,and the interference can be effectively suppressed in the condition of small snapshots. At the same time,it combines the penalty function to constrain the adaptive pattern to approximate the expected static pattern. The proposed algorithm has good sidelobe characteristics and improves the signal to interference plus noise ratio（ SINR） of the output in the condition of small snapshots. Simulation results confirm the feasibility and effectiveness of the proposed algorithm.