平移嵌套阵列稀疏贝叶斯学习角度估计算法

针对阵元间互耦效应导致嵌套阵列测向性能下降的问题，该文提出两种不同的平移嵌套阵列结构，在保证产生虚拟阵列无孔的条件下，通过对原二级嵌套阵列阵元位置进行调整，形成平移嵌套阵列，提高了原二级嵌套阵列的稀疏性，降低了阵元间的互耦效应，扩展了原嵌套阵列的测向自由度。在空间辐射源数目未知条件下，建立了平移嵌套阵列稀疏贝叶斯学习(SBL)算法模型，对形成的虚拟阵列接收数据进行处理，获得角度估计，有效提高了原嵌套阵列测向算法的测向性能。仿真实验表明，平移嵌套阵列自由度高于原嵌套阵列，在低信噪比、小快拍数、存在互耦影响条件下，基于稀疏贝叶斯学习的平移嵌套阵列测向算法测向精度优于原嵌套阵列测向算法，并且提高了原嵌套阵列测向算法的角度分辨率。

A Direction of Arrial Estimation Algorithm for Translational Nested Array Besed on Sparse Bayesian Learning

The performance of direction finding for nested array degrades due to the mutual coupling effect among the elements. Two different translational nested array structures are proposed. In order to ensure that the virtual array has no holes, a translational nested array is formed by adjusting the positions of the original two level nested array elements. It improves the sparsity of the original two level nested array, reduces the mutual coupling effect, and extends the direction finding freedom of the original nested array. Under the condition of unknown number of spatial radiation sources, a Sparse Bayesian Learning (SBL) model for translational nested array is established. Through this model, the received data of the virtual array is processed, the DOA estimation is obtained and the direction finding performance of the original nested array direction finding algorithm is effectively improved. Simulation results show that the translational nested array has higher degree of freedom than the original nested array. Under the scenarios of low Signal-to-Noise Ratio (SNR), snapshot deficiency, and mutual coupling effect, the performance of direction finding algorithm for translational nested array based on Sparse Bayesian Learning is better than that of direction finding algorithm for the original nested array. The angle resolution of direction finding algorithm for the original nested array is improved.