Nested arrays exhibit higher spatial resolution (SR) and enhanced degrees-of-freedom (DOFs) with fewer sensors, and they have been utilized for direction-of-arrival (DOA) estimation of both far-field (FF) and near-field (NF) sources. In this study, an improved symmetric nested array configuration with a given number of sensors was developed, which achieved increased consecutive and unique lags and thus resolved more targets than the actual number of inherent array sensors. In particular, the analytical expressions of the number of consecutive lags, the number of unique lags, and the virtual array aperture were derived for quantitative evaluation and comparison, as well as the corresponding array composition parameters of the optimal array geometry were obtained. In the mixed sources localization scheme, a special cumulant matrix was constructed to eliminate the range parameter in the NF steer vectors by exploiting the symmetric feature. Both subspace and sparse reconstruction techniques were exploited in order to directly obtain the DOAs of both FF and NF sources. With the estimated DOAs and the covariance matrix of the array output, the NF sources could be identified, and the corresponding range parameters could also be obtained by a one-dimension (1-D) spectrum search scheme. Numerous simulation results demonstrated that the proposed array showed a remarkable performance in terms of estimation accuracy, SR capacity, and numerous DOFs compared to state-of-the-art symmetric nested arrays.

嵌套阵列具有较少的传感器，具有更高的空间分辨率（SR）和增强的自由度（DOF），并且已被用于远场（FF）和近场的到达方向（DOA）估计（NF）来源。在这项研究中，开发了一种具有给定数量传感器的改进的对称嵌套阵列配置，该配置实现了增加的连续滞后和唯一滞后，从而比固有阵列传感器的实际数量解决了更多目标。特别地，导出了连续滞后次数，唯一滞后次数和虚拟阵列孔径的解析表达式，以进行定量评估和比较，并获得了最佳阵列几何形状的相应阵列组成参数。在混合源本地化方案中，通过利用对称特征，构造了一个特殊的累积矩阵，以消除NF转向矢量中的距离参数。为了直接获取FF和NF源的DOA，都利用了子空间和稀疏重建技术。利用估计的DOA和阵列输出的协方差矩阵，可以识别NF源，还可以通过一维（1-D）频谱搜索方案获得相应的范围参数。大量的仿真结果表明，与最新的对称嵌套阵列相比，所提出的阵列在估计精度，SR容量和众多自由度方面表现出了卓越的性能。为了直接获取FF和NF源的DOA，都利用了子空间和稀疏重建技术。利用估计的DOA和阵列输出的协方差矩阵，可以识别NF源，还可以通过一维（1-D）频谱搜索方案获得相应的范围参数。大量的仿真结果表明，与最新的对称嵌套阵列相比，所提出的阵列在估计精度，SR容量和众多自由度方面表现出了卓越的性能。为了直接获取FF和NF源的DOA，都利用了子空间和稀疏重建技术。利用估计的DOA和阵列输出的协方差矩阵，可以识别NF源，还可以通过一维（1-D）频谱搜索方案获得相应的范围参数。大量的仿真结果表明，与最新的对称嵌套阵列相比，所提出的阵列在估计精度，SR容量和众多自由度方面表现出了卓越的性能。