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基于特征空间的信源数估计方法

Eigenspace-based approach to determining the number of sources

  • 摘要: 准确估计信源数对于高分辨算法具有重要意义。信源数的估计方法大多为对数据协方差矩阵的特征值序列按某种标准设定门限, 将满足该门限的特征值个数作为信源数的估计值。从特征值序列和特征向量两个方面推导分析了两信源空间方位相近时特征空间的特性。在此基础上提出了一种综合利用特征值和特征向量信息估计信源数的新方法。仿真结果表明:当信源方位相近(小于半功率宽度)时, 相对于仅利用特征值信息进行估计的方法, 新方法体现出了很好的优越性;其它条件下, 新方法与最小描述长度法(Minimum Description Length, MDL)性能相当。

     

    Abstract: Determining the number of sources is very important to high resolution algorithm.To solve this problem, the general way is to count the number of eigenvalues satisfying the threshold calculated from the sample covariance matrix by some criteria.In this paper, the character of eigenspace is concerned in both eigenvalues and eigenvectors for the sources spaced closely.As a result, a new approach is proposed, which determines the number of sources by synthetic use of eigenvectors as well as eigenvalues.Performance of the new approach is superior to eigenvalue-based approaches for the sources spaced closely(less than half power beam width), and equal to MDL(Minimum Description Length) otherwise.Simulation results are presented to confirm the conclusion.

     

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