基于稀疏采样阵列优化的APG-MUSIC算法

摘要

针对稀疏阵列下2维波达方向(DOA)估计问题,该文提出一种基于稀疏采样阵列优化的加速逼近梯度(APG)算法与多重信号分类(MUSIC)算法相结合的2D-DOA估计方法.首先,建立稀疏阵列下的2D-DOA估计信号模型,并证明其具备低秩特征,满足零空间性质(NSP).其次,为提高稀疏阵列下矩阵填充方法重构接收信号矩阵性能和以此为基础的2D-DOA估计精度,提出基于遗传算法(GA)的稀疏采样阵列优化方法.最后,将APG和MUSIC算法相结合,在重构完整平面阵列接收信号矩阵的基础上完成2维波达方向估计.计算机仿真结果表明,该方法在保证2维波达方向估计精度前提下,大幅提高阵元利用率,有效降低空间谱平均旁瓣,与常规2D-DOA估计方法相比具有优势.%A novel Two Dimension Direction Of Arrive (2D-DOA) estimation method based on sparse sampling array optimization is proposed, which is combined with Accelerated Proximal Gradient (APG) and MUltiple SIgnal Classification (MUSIC). First, a 2D-DOA estimation signal model for sparse array is established, and its low rank feature and Null Space Property (NSP) are analyzed. Then, a sparse sampling array optimization method based on Genetic Algorithm (GA) is studied to enhance the performance of Matrix Completion (MC) and DOA. Finally, APG and MUSIC are employed to reconstruct the received signal matrix and estimate the direction of wave arrived, respectively. Computer simulation results show that the proposed method improves the utilization rate of array and reduces the average side lobe of spatial spectrum effectively, compared with the conventional 2D-DOA methods.