一种最差情况下性能最优化的特征分析自适应波束形成方法

自适应波束形成在弱目标检测和空域滤波中应用广泛。然而在实际海洋环境中,失配情况比较普遍,往往会导致传统自适应波束形成方法性能明显下降。针对此问题,本文基于特征分析重构观测信号协方差矩阵来抑制干扰对弱目标检测的影响,在此基础上进一步利用凸优化算法来提高波束形成方法的鲁棒性。数值仿真和海试数据处理结果表明,本文所提方法在抑制干扰对弱目标影响的基础上,在阵元位置平均误差不超过入射信号中心频率对应波长的40%的情况下,仍可以鲁棒地检测目标信号。同时,该方法显著提高了自适应波束形成方法的输出信干噪比,且受对角加载量取值的影响较小。 

Eigenanalysis-based adaptive beamforming using worst-case performance optimization

Robust adaptive beamforming is an efficient way for both spatial filtering and weak target detection in the presence of interference and noise,particularly in oceanic engineering where mismatch most often degrades the performance of conventional adaptive beamforming.In the proposed method,the covariance matrix is re-constructed based on eigenanalysis,using a more stable and practical criterion.Convex optimization is implemented on the reconstructed covariance matrix to further improve the robustness.Numerical simulations and experimental results show that the proposed method can efficiently suppress the interference and robustly detect the weak target under the condition that average sensors' position error is less than 40%of incident signal wavelength.The proposed robust adaptive beamforming has higher output signal to interference plus noise ratio,and is less sensitive to the diagonal loading factor.