针对二级嵌套阵列中的紧凑阵元结构易受互耦效应影响的问题，提出了两种不同的嵌套阵列结构改进方法：连续平移嵌套阵列和间隔平移嵌套阵列。通过对原有二级嵌套阵列阵元位置进行调整，形成了两种不同的平移嵌套阵列结构，这两种结构对应的差分共阵均"无孔"，并且测向自由度和阵列稀疏度均大于原二级嵌套阵列。针对嵌套阵列的差分共阵测向模型为单测量矢量模型，稀疏贝叶斯学习测向算法复杂度高的问题，提出了平滑重构稀疏贝叶斯学习算法。该算法通过空间平滑重构将单测量矢量模型变为多测量矢量模型，降低了观测矩阵的维度，减小了计算复杂度。算法求解时，通过对变换后的观测矩阵进行奇异值分解，进一步降低了观测矩阵维度，利用稀疏贝叶斯学习算法估计辐射源角度。仿真表明，在信噪比和采样数相同的条件下，该算法收敛速度比单测量矢量稀疏贝叶斯学习（SMV-SBL）算法快，且测向精度高于SMV-SBL算法和空间平滑多重信号分类（MUSIC）算法；存在互耦影响时，两种平移嵌套阵列比原嵌套阵列受互耦影响小。
The compact array structure in the two-level nested array is subject to mutual coupling effects. To solve this problem, a method for improving two different nested array structures (continuous translational nested array and spaced translational nested array structures) is proposed. By adjusting the position of the element of the original two-level nested array, two different translational nested array structures are formed. The difference coarrays of these two structures are both "no hole", and the degree of freedom and array sparsity are larger than those of the original nested array. The direction finding model for difference coarray of the nested array is a single measurement vector model; therefore, the sparse Bayesian learning direction finding algorithm has high complexity. In view of this problem, a restructure sparse Bayesian learning algorithm is proposed. In this algorithm, the single measurement vector model is changed into a multi-measurement vector model via spatial smoothing. Singular value decomposition is applied to the transformed observation matrix to reduce the dimensionality and the computational complexity. Simulation results show that when the signal-to-noise ratio and number of samples are the same, the proposed algorithm converges faster than Single Measurement Vector Sparse Bayesian Learning (SMV-SBL), and the accuracy of direction finding with the proposed algorithm is higher than that with SMV-SBL and spatial smoothing Multiple Signal classification (MUSIC) algorithm. In the presence of mutual coupling, the two translational nested arrays are less affected by the coupling than the original nested array.
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