基于圆阵虚拟阵列平移的相干信源数目估计

Coherent Sources Number Estimation Based on Circular Array Virtual Array Translation

在空间谱估计中,多数基于特征子空间的算法均要求信源数目精确已知,当信源数估计出现误差时算法性能将严重下降。针对在相干信源存在时信息论准则估计信源数目不准,且基于线阵的相干信源估计算法不能直接应用于圆阵的问题,提出一种圆阵相干信源数估计算法。通过阵列的虚拟平移实现信源的完全解相干,并利用信息论准则估计解相干后的信源数目,从而避免进行圆阵到线阵的模式空间变换,降低算法复杂度。实验结果表明,在不损失阵列孔径情况下,该算法能正确估计出相干信源数目,并且在低信噪比和小快拍数下性能明显优于FBSS_M DL和Toep_M DL算法。

In spatial spectrum estimation,most of the algorithms based on feature subspace require the number of sources to be accurately known.When the number of sources is estimated to be error,the performance of the algorithm is seriously invalid.For the existence of coherent sources,the information theory criterion estimates the number of sources is not accurate,and the linear array-based coherent source estimation algorithm can not be directly applied to the circular array.A circular array coherent source number estimation algorithm is proposed.Through the virtual translation of the array,the complete decoherence of the source is realized,and the information theory criterion is used to estimate the number of sources after decoherence.The mode space transformation of the circular array to the linear array is not needed,and the algorithm complexity is significantly reduced.Experimental results show that the proposed algorithm can correctly estimate the number of coherent sources without loss of array aperture,and the performance is better than FBSS_MDL and Toep_MDL algorithms at low SNR and small snapshot.