基于特征矢量稀疏重构的欠定DOA估计算法

The Underdetermined DOA Estimation Algorithm Based on Sparse Reconstruction of Eigenvector

针对信源数多于阵元数,阵列测向欠定问题,利用最小冗余线阵的阵列扩展能力,以较少的阵元获得较大的阵列孔径,同时将最小冗余线阵与压缩感知理论相结合,提出一种基于特征矢量稀疏重构的欠定DOA估计算法。所提算法能以较少的阵元数估计更多信源的来波方位,具有信源过载能力,同时能降低稀疏重构运算的复杂度,增强了算法的鲁棒性、精确性,性能优于MUSIC算法,实验结果表明该方法是有效的。

To improve the accuracy of the underdetermined DOA estimation when source numbers are more than the number of arrays, the minimum redundancy linear array （MRLA） is used to obtain larger antenna aperture through a smaller number of array sensors. MRLA is combined with Compressive Sensing （CS） algorithm to estimate underdetermined DOA signals based on sparse reconstruction of eigenvector. The proposed method can estimate more sources with a low computation complexity. The experimental results show this new method has a better perform- ance than the MUSIC algorithm in the aspects of accuracy and robustness.