基于压缩感知的波束域DOA估计

Compressive Sensing Based Beamspace DOA Estimation

针对传统波达方向角(DOA)估计算法需要大量采样数据从而导致较高计算复杂度的问题,基于压缩感知理论,利用目标信号空域稀疏性,提出一种基于波束域的多测量矢量欠定系统正则化聚焦求解DOA估计算法。该算法将压缩信号从阵元域映射至波束域,一定程度上克服了稀疏重构算法无法用于低信噪比情况下的缺陷。数值仿真表明,与传统的Capon,MUSIC和l1-SVD算法相比,所提算法可对相干信号进行有效DOA估计,具有较高角度分辨力和估计精度;与RMFOCUSS和l1-SVD算法相比,所提算法具有较低计算复杂度。

The traditional Direction of Arrival（ DOA） estimation algorithms require a large amount of sampling data,which causes high computational complexity.To address this problem,based on the compressive sensing theory,a beamspace based Regularized Multi-vector Focal Undetermined System Solver（ RMFOCUSS） DOA estimation algorithm is proposed,which uses the spatial sparsity characteristic of targets of interest.The proposed algorithm maps the received compressed signals from the element-space to the beamspace,which overcomes the flaw that the sparse reconstruction algorithm cannot be used under the conditions of low SNR to some extent.Numerical simulations demonstrate that： 1） Compared with the traditional Capon,MUSIC and l1-SVD algorithms,the proposed algorithm can effectively carry out DOA estimation of the coherent signals with higher angle resolution and estimation accuracy; and 2） Compared with the element-space based RMFOCUSS and l1-SVD algorithms,the proposed method has a lower computational complexity.