针对稀疏信号的超分辨方位估计问题,提出一种可变因子的稀疏近似最小方差算法(α-Sparse Asymptotic Minimum Variance,简记为SAMV-α)。该算法利用一个折衷参数进行最大似然估计值和稀疏性能的折衷处理,在迭代过程中改变稀疏近似最小方差算法(Sparse Asymptotic Minimum Variance,SAMV)的指数因子,得到强稀疏性能和超低旁瓣的方位谱图,实现邻近目标的超分辨方位估计和相干处理性能,且无需预估角度和信源数目等先验信息,并且折衷参数的取值为0到1之间,取值区间明确,避免了稀疏信号处理算法中正则因子选取困难的弊端。计算机仿真表明SAMV-α算法方位估计性能明显优于波束扫描类算法和子空间类算法,与同类型稀疏信号处理类算法相比仍具有较高的方位估计精度,同时对于邻近声源分辨能力,SAMV-α算法较SAMV-1算法性能提高约3dB。海上试验数据处理给出了分辨率更高的方位时间历程(Bering-Time Recording,BTR)图,有效验证了SAMV-α算法的性能。 To solve the problem of super-resolution azimuth estimation of sparse signals, a variable sparse Asymptotic Minimum Variance (SAMV-α) algorithm is proposed. The trade-off between maximum likelihood estimation and sparse performance is performed by a compromise parameter. The exponential factor of Sparse Asymptotic Minimum Variance (SAMV) is changed during iteration to obtain strong sparse performance and ultra-low sidelobe , Which achieves the performance of super-resolution azimuth estimation and coherent processing of the neighboring objects without any priori information such as the estimated angle and the number of sources, and the value of the compromise parameter is between 0 and 1, the interval of values ​​is clear, Avoiding the disadvantages of selecting the regular factor in the sparse signal processing algorithm. Computer simulation shows that the performance of SAMV-α algorithm is better than that of the beam scanning algorithm and subspace algorithm. Compared with the same type of sparse signal processing algorithms, the SAMV-α algorithm still has higher azimuth estimation accuracy. Meanwhile, for the adjacent sound source resolution, SAMV -α algorithm improves the performance of SAMV-1 algorithm by about 3 dB. The sea trial data processing gives a higher resolution Bering-Time Recording (BTR) graph, which effectively verifies the performance of the SAMV-α algorithm.