This study addresses the problem of direction-of-arrival (DOA) estimation of coherent signals via sparse parameter estimation. Since many sparse methods provide good performances regardless of signal correlations and array geometry, they can be considered as candidates for DOA estimation of coherent signals impinging on a sensor array with arbitrary geometry. However, their straightforward applications require high computational loads especially for two-dimensional (2D) DOA estimation. Two efficient methods based on sparse parameter estimation are herein presented; one is a combined approach of sparse estimation and the RELAX algorithm extended for 2D DOA estimation and the other relies on the adaptive 2D grid refinement and power update control. Numerical simulations are performed to demonstrate the efficiency of the proposed methods using a uniform circular array for both 1D and 2D DOA estimation cases. It is shown that sparse asymptotic minimum variance (SAMV)-RELAX, a combined approach of SAMV and RELAX, outperforms SAMV and multi-stage SAMV in 2D scenarios without suffering from plateau effects for off-grid signals and that its computational load is significantly lower than those of SAMV and multi-stage SAMV. In addition, SAMV-RELAX does not require the difficult selection of grid parameters for fine DOA estimation unlike the multi-stage approach.

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基于有效稀疏参数估计的相干信号二维DOA估计方法

这项研究解决了通过稀疏参数估计的相干信号到达方向（DOA）估计的问题。由于许多稀疏方法均提供良好的性能，而与信号相关性和阵列几何形状无关，因此可以将它们视为对撞击到具有任意几何形状的传​​感器阵列上的相干信号进行DOA估计的候选方法。但是，它们的直接应用需要很高的计算量，尤其是对于二维（2D）DOA估计而言。本文介绍了两种基于稀疏参数估计的有效方法：一种是稀疏估计和RELAX算法的组合方法，可扩展用于2D DOA估计，另一种方法则依赖于自适应2D网格细化和功率更新控制。进行了数值模拟，以证明所提出的方法在1D和2D DOA估计情况下使用均匀圆形阵列的效率。结果表明，稀疏渐近最小方差（SAMV）-RELAX（SAMV和RELAX的组合方法）在2D场景中胜过SAMV和多阶段SAMV，而不受离网信号的平稳影响，并且其计算量显着降低而不是SAMV和多阶段SAMV。另外，与多阶段方法不同，SAMV-RELAX不需要为精细的DOA估计而艰难地选择网格参数。在2D场景中胜过SAMV和多级SAMV，而不受离网信号的平稳影响，并且其计算量明显低于SAMV和多级SAMV。另外，与多阶段方法不同，SAMV-RELAX不需要为精细的DOA估计而艰难地选择网格参数。在2D场景中胜过SAMV和多级SAMV，而不受离网信号的平稳影响，并且其计算量明显低于SAMV和多级SAMV。另外，与多阶段方法不同，SAMV-RELAX不需要为精细的DOA估计而艰难地选择网格参数。