基于多级维纳滤波器的信源参数估计

Source parameter estimate based on multistage wiener filter

为了解决传统的MUSIC算法需要计算相关函数并对其进行特征值分解或奇异值分解而导致的计算量增加问题,本文基于多级维纳滤波器快速子空间分解方法,分别在远场环境和近场环境中对信源参数进行了有效估计,并提出在一定条件下,可以采用该方法代替对相关函数的计算和分解。该方法计算量小,特别是在阵元数和快拍数较多的情况下优势更加明显。仿真实验证明了该方法的有效性。

The traditional MUSIC algorithm requires the covariance matrix and its eigendecomposition or singular value decomposition that result in the increase of the computational complexity. To overcome such problem, a method for source parameter estimation in the environments of far-field and near-field was proposed based on multistage Wiener filters. It can replace the computation and decomposition of the covariance matrix under certain conditions. It has less computational complexity, which is particularly significant in the situation of more numbers of arrays and snapshots. The proposed method is validated by case studies.