中文摘要：

直接将压缩感知（compressed sensing,CS）思想应用到相干信源二维波达方向（direction of arrival,DOA）估计中会带来高计算复杂度的问题。为了解决这一问题,提出了一种基于降维稀疏重构的二维DOA估计方法,该方法利用特殊阵列结构将二维冗余字典构建问题转化为一维冗余字典的构建,同时提出了一种基于子字典空间谱重构的配对算法,从而在极大降低算法计算复杂度的同时,提高了配对成功概率。仿真结果表明,该方法对相干信源具有接近于克拉美罗下界（Cramér-Rao lower bound,CRLB）的估计性能,即使是在低信噪比、少快拍数和小角度间隔的情况下,仍有良好的估计性能。

英文摘要：

The problem of high computational complexity will be caused if compressed sensing（CS）is directly applied to two-dimensional（2-D）direction of arrival（DOA）Estimation of coherent sources.To solve this problem,a 2-D DOA estimation method based on reduction dimension sparse reconstruction（RDSR）is proposed.The proposed method converts the construction of a 2-D redundancy dictionary into that of a 1-D dictionary by using the special array structure.In addition,apair-matching scheme is proposed based on spatial spectrum reconstruction of the sub-dictionary.Therefore,the proposed method not only reduces the computational complexity but also improves the pairing probability of success.Simulation results show that the estimated performance of the method is close to the Cramér-Rao lower bound（CRLB）,even in the case of low signal-to-noise ratio（SNR）,small number of snapshots and small angle interval,the estimation performance is still good.