The source localization problem was cast as the problem of recovering a joint sparse representation. It used the singular value decomposition of the data matrix to summarize multiple time and frequency samples,then imposed the smoothed l0 norm to enforce sparsity and used a fixed-point iteration approach to solve the joint optimization problem. The proposed algorithm has the following advantages: improved robustness to noise, improved computation efficiency, robustness to limited number of samples, robustness to correlated sources, no requirement of accurate initialization. The performance of the proposed method was compared to standard spectrum based approaches and other sparse based methods.%将信号DOA的估计问题转换为一个联合稀疏表示的求解问题.通过对接收数据矩阵的奇异值分解实现各时间和频率快拍数据的联合；然后通过求解一个平滑l0范数稀疏约束的联合优化问题实现信号源DOA的估计.基于稀疏表示的信号DOA估计方法不仅能够有效地减少数据量,而且具有以下优点:更好的抗噪声性能、更高的计算效率、适用于相关和非相关信号.通过与其他DOA估计方法的比较,表明了该方法的有效性和优越性.
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