基于子空间的后验稀疏约束迭代DOA估计方法

A Subspace-Based Recursive Approach to DOA Estimation with Sparsity Constraint

子空间波达方向 (DOA)估计方法的一个缺点是在子空间分解过程中难以利用信号的相关信息或有关DOA估计的先验信息改善DOA估计的性能。结合子空间方法和协方差矩阵拟合方法 ,利用信号相关信息和DOA估计的稀疏约束信息 ,研究了一种新的不相关窄带信号DOA估计方法。理论分析和仿真实验结果表明 ,这种迭代DOA估计方法一般经过几次迭代就能获得稳定的高分辨率DOA估计。

One disadvantage with subspace-based DOA estimate method is that it's difficult to incorporate prior knowledge of the signal correlation into the eigendecomposition. Herein, we propose an estimator that combines ideas from subspace and covariance matching method, which makes it possible to incorporate the prior knowledge of signal correlation and sparsity of the DOA estimation into the estimator. Theory analysis and numerical results illustrate the robust and high resolution character of the estimator.