Direction of arrival (DOA) estimation from sparse signal representation has gained much attention in recent years. In this, the spatial signal is reconstructed by using a Compressive sensing (CS) framework. CS is a new paradigm by which the signal acquisition and reconstruction are carried out at sub-Nyquist rates. The limitation of the Nyquist sampling theorem is overcome by sparse sampling and reconstruction. This paper uses the CS framework in DOA estimation to reduce the underlying computational cost in the reconstruction process. Many reconstruction algorithms have been described in the past years. However, the comparative study on the reconstruction performances for CS-based DOA estimation is lacking. This work primarily concentrates on different reconstruction algorithms that are utilized in CS. The performance of various reconstruction algorithms for single snapshot DOA estimation is compared in this paper. Different parameters, like finding the target failure rate, Root Mean Square Error (RMSE), execution time are considered to evaluate the performance of the techniques.

近年来，基于稀疏信号表示的波达方向（DOA）估计备受关注。在这种情况下，空间信号是通过使用压缩感知 (CS) 框架来重建的。CS 是一种以亚奈奎斯特速率进行信号采集和重建的新范例。稀疏采样和重构克服了奈奎斯特采样定理的局限性。本文在 DOA 估计中使用 CS 框架来降低重建过程中的底层计算成本。在过去的几年中已经描述了许多重建算法。然而，缺乏对基于 CS 的 DOA 估计重建性能的比较研究。这项工作主要集中在 CS 中使用的不同重建算法上。本文比较了各种重构算法在单快照DOA估计中的性能。不同的参数，如寻找目标故障率、均方根误差 (RMSE)、执行时间等，都被用来评估技术的性能。