阵元失效下稀疏阵列的二维DOA估计算法

Two-Dimensional DOA Estimation Algorithm for Sparse Arrays under Sensor Failures

本文针对二维稀疏阵列在阵元失效条件下,因数据缺失导致虚拟阵列连续性被破坏及自由度下降的问题,提出了一种二维DOA估计算法。首先基于二维差分共阵构建虚拟阵列,然后利用解耦原子范数最小化理论,以矩阵填充的形式恢复协方差矩阵数据,实现对虚拟阵列中丢失虚拟阵元的内插,最后采用SS-MUSIC算法进行多信源的二维DOA估计。所提方法弥补了物理阵元失效所造成的影响,恢复了原始虚拟阵列的完整孔径特性,保持了虚拟阵列的自由度,从而确保了较高精度的二维DOA估计性能。仿真实验结果表明,在相同阵元数量及阵元失效情况下,本文提出的算法相比已有方法能有效地估计更多信源,并在小快拍数和低信噪比条件下表现出更高的稳健性,最大限度地保留并利用了稀疏阵列在二维DOA估计中的自由度优势。

To address the problem of the destruction of virtual array continuity and the degradation of degree of freedom due to missing data in two-dimensional sparse array under the conditions of sensor failures,a two-dimensional DOA estimation algorithm is proposed.Firstly,the virtual array is constructed based on the two-dimensional difference coarray,and then the covariance matrix data is recovered in the form of matrix completion by using decoupled atomic norm minimization to realize the virtual array interpolation.Finally,the SS-MUSIC algorithm is used for the two-dimensional DOA estimation of multiple sources.The proposed method compensates for the effects caused by the failure of physical sensors.It recovers the complete aperture characteristics of the original virtual array and maintains the degree of freedom of the virtual array,which ensures a higher-precision two-dimensional DOA estimation performance.Simulation results demonstrate that under the same number of physical elements and sensor failure,the proposed algorithm can effectively estimate more sources compared with the existing methods,and exhibits higher robustness under the conditions of a small number of snapshots and low signal-to-noise ratio.This approach maximally retains and utilizes the degree of freedom advantage of sparse arrays in the two-dimensional DOA estimation.