利用最小冗余对称阵列的近场源定位算法

在阵元数目有限的情况下，针对近场源定位中的阵列孔径和阵元数损失问题，文中突破阵元间距为1/4波长的限制，提出了基于最小冗余对称阵列的协方差矩阵重构算法．该算法构建只与方位角有关的四阶累积量矩阵，通过多重信号分类算法来估计信号方位角；然后根据估计出的信号角度在距离维上进行搜索，估计出距离参数．该算法扩展了阵列的孔径，提高了阵列的自由度．仿真结果表明，该算法可以估计更多的信源数目，拥有较高的估计性能和空间分辨率，且只需进行一维搜索，避免了二维参数配对．

Algorithm for near-field source localization using the minimum redundant symmetric array

For the issue of aperture and array elements losses in near-field source localization when having a limited number of sensors, a covariance matrix reconstruction method is proposed based on the symmetric minimum redundant array which allows the element spacing not to be limited to the quarter wavelength. A fourth-order cumulant matrix is constructed which is related only to the azimuth to estimate the azimuth angles of sources by the multiple signal classification (MUSIC) algorithm. Then the ranges of sources can be estimated by searching the spectral peak according to the estimated angles. The algorithm extends the aperture of the array and enhances the degree of freedom. Simulation results show that the algorithm can estimate a much larger number of sources and has a better estimated performance and spatial resolution. Besides, the algorithm only requires one-dimensional search, which avoids two-dimensional parameter matching.