一种低副瓣稳健自适应波束算法

本文提出一种新型低副瓣自适应波束形成算法，解决了传统自适应波束形成器在干扰和高信噪环境下算法性能急剧下降的问题，并降低了快拍数对算法稳健性的影响。该算法基于标准Capon波束形成器，利用消除空域噪声的方法提高期望矢量的重估精度，并结合功率估计算法重构出干扰噪声协方差矩阵；然后使用特征干扰相消算法二次重构噪声协方差矩阵得到最优权值，增强算法在低样拍下的稳定性；最后对最优权值进行切比雪夫加权和二次约束实现了零陷加宽。仿真实验结果表明：新算法计算量小，旁瓣电平低，降低了在干扰运动和导向矢量失配时快拍数和信噪比对性能的影响。

A Low Sidelobe Robust Adaptive Beamforming Algorithm

In this paper, a novel adaptive beamforming algorithm with low sidelobe is proposed, which solves the problem that the performance of the traditional adaptive beamforming algorithm declines sharply in the environment of interference and high signal-noise, and reduces the effect of the fast beat number on the robustness of the algorithm. Based on the standard Capon beam formers, the re-estimation accuracy of the expected vector is improved by eliminating spatial noise, and the interference noise covariance matrix is reconstructed by combining with the power estimation algorithm. Then, the characteristic interference cancellation algorithm is used to reconstruct the noise covariance matrix to obtain the optimal weight, which enhances the stability of the algorithm under low sample shoot. Finally, Chebyshev weighting and quadratic constraint are applied to the optimal weights to achieve the null widening. The simulation results show that the new algorithm has the advantages of small computation and low sidelobe level, which reduces the influence of fast beat number and signal-to-noise ratio on the performance of interference motion and steering vector mismatch.