When l 1-SVD algorithm based on 1 norm for sparse reconstruction was used for DOA estimation with low SNR,the result was less sparse.There were pseudo peaks in spatial spectrum,which had a bad effect on estimating DOA accurately.To solve these problems,l 1-SVD algorithm was modified.A novel sparse recon-struction algorithms based on weighted 1 norm was proposed.Firstly,the weight vector was chosen from the spatial spectrum obtained by fourier transform on sampled signals.Secondly,the product between weights and elements in the result was made to get weighted 1 norm.Weighted 1 norm was taken as target function for mini-mization so that to facilitate the sparsity of result.Experimental results showed that the way to choose weights had low computation complexity.The weighted l 1-SVD algorithm could suppress pseudo peaks and improved the precision of DOA estimation effectively.In addition,the algorithm had better performance with of snapshots in-creasing.%针对基于 l 1范数的 l 1-SVD 稀疏重构波达方向(DOA)估计算法在低信噪比条件下，求解得到的解矢量稀疏性较差，空间谱中存在较多伪峰，对 DOA 的正确估计造成干扰的问题，对 l 1-SVD 算法进行改进，提出了基于加权 l 1范数的稀疏重构 DOA 估计算法。该算法首先对采样信号进行空间傅里叶变换，由空间傅里叶变换得到的空间谱选取权值矢量；再对 l 1-SVD 算法中解矢量的各元素进行加权，以解矢量的加权 l 1范数作为最小化的目标函数，从而促进结果的稀疏性。仿真分析表明，该算法的权值选取过程所需的计算量小，加权处理后的 l 1-SVD 算法能够有效地抑制伪峰，提高 DOA 估计精度，且在低信噪比条件下，该算法的性能随快拍数的增大而提高。
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