稀疏重构混合源参数估计方法

针对近远场混合源定位问题，本文提出了一种基于稀疏信号重构的信源参数估计算法。该算法首先通过对接收信号的协方差矩阵进行稀疏重构估计出远场信源参数，接着采用协方差分离技术将近场源和远场源分离，最后利用均匀线阵的对称性和稀疏信号重构估计近场信源参数。该算法避免了二维谱峰搜索和近场源参数配对，也无需构造高阶累积量，降低了计算复杂度。仿真结果表明，该算法的空间分辨能力和混合源参数估计精度均高于基于子空间的混合源参数估计方法。 

Method of Parameters Estimation for Mixed Sources Based on Sparse Reconstruction

In this letter, we present a parameters estimation algorithm based on the sparse signal reconstruction to cope with the mixed sources localization problem. First, the far-field sources parameters are estimated by the sparse reconstruction of the covariance matrix of the received signal. Then the covariance separation technique is exploited to separate the near-field and the far-field sources. Finally, the near-field source parameters are estimated by the symmetric property of the uniform linear array geometry and the sparse signal reconstruction. Two-dimensional peak searching and the matching of the near field sources parameters as well as the construction of the high-order cumulants are avoided, which reduce the computational complexity. The simulation results show that the spatial resolutions and the estimation accuracy of the mixed sources parameters are higher than those based on subspace.