基于最小冗余线阵的ROC-ML测向方法

ROC-ML Direction Finding Based on Minimum Redundancy Linear Arrays

在冲击噪声环境下,通过构造数据把最小冗余线阵的共变矩阵虚拟成多阵元均匀线阵的共变矩阵。在虚拟的阵列流型和扩展的共变矩阵基础上,推导出了基于最小冗余线阵和共变矩阵的最大似然算法。由于所提方法可虚拟出更多有效阵元数,扩展了阵列孔径,可以提高最大似然算法估计性能。为了快速求解所提的基于最小冗余共变矩的ML测向算法,设计了自适应文化算法求解所提出的目标函数。Monte-Carlo仿真试验证明了所提的测向算法具有估计性能好、抗冲击噪声能力强且可以用较少的阵元测出更多的信源。

According to structural characteristics of covariation matrix of the minimum redundant array, a covariation matrix of virtual multi-element uniform linear array is constructed in the presence of impulsive noise. Based on array manifold of virtual multi-element uniform linear array and recon- structed covariation matrix, a novel maximum likelihood algorithm is proposed. The proposed algorithm using few elements and expands the number of effective aperture array, and significantly improves the performance of the original maximum likelihood algorithms. In order to fit the proposed direction finding algorithm based on the minimum redundant array and eovariation matrix, an adaptive cultural algorithm is designed for objective function of direction finding. Monte-Carlo simulations proves that the proposed algorithm has some good performance such as high resolution in the presence of impulse noise and using a small number of elements to detect more sources.