基于稀疏重构的共形阵列稳健自适应波束形成算法

摘要

Adaptive beamforming techniques for conformal arrays suffer from poor universality, difficulty to maintain the main beam and high computational cost. A novel robust adaptive beamforming algorithm for conformal arrays based on sparse reconstruction is proposed to alleviate the existing problems. Firstly, by introducing the Asymptotic Minimum Variance (AMV) criterion, the Interference-Plus-Noise (IPN) covariance matrix reconstruction is realized in a sparse way. Secondly, the Steering Vector (SV) of the Signal Of Interest (SOI) is estimated. Finally, the optimal weight coefficients are achieved. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior output performance over the existing adaptive beamforming methods for conformal arrays in a large range of Signal to Noise Ratio (SNR) of the SOI. Moreover, the proposed algorithm needs fewer snapshots with a lower computational cost and has a faster convergence rate.%针对共形阵列天线自适应波束形成中存在的通用性差、主瓣保形困难、计算复杂度高等问题，该文提出一种基于稀疏重构的稳健自适应波束形成算法。该算法通过引入渐进最小方差准则，实现了干扰加噪声协方差矩阵的稀疏重构，并得到期望方向上的导向矢量估计，进而求得波束形成器的最优权矢量。该算法无需复杂的子阵分解或虚拟映射变换，适用于任意阵列形状。仿真实验验证了该算法不仅保证了期望的主瓣响应，同时对指向误差有较好的稳健性。与现有算法相比，该算法所需采样快拍数少，计算复杂度低，收敛速度快，在较大的输入信噪比范围内达到了较好的阵列输出性能。