三种修正的宽带信号子空间谱估计方法

宽带子空间谱估计法存在着信号子空间维数扩展问题，给信号子空间和噪声子空间划分及子空间谱估计带来困难。本文提出了信号子空间维数估计法、噪声子空间加权法和扩展MUSIC法3种修正的子空间谱估计方法，它们均有效地解决了这一问题。信号子空间维数估计法用包含信号99．9％以上功率的特征值数目来估计信号子空间维数，正确地划分了子空间。噪声子空间加权法和扩展MUSIC法不用划分子空间，而是给噪声子空间特征向量或全空间特征向量加权，减小了子空间维数扩展的影响。计算机仿真验证了3种方法的有效性。

On Overcoming Signal-Subspace Dimension Expanding in Wide-Band Signal-Subspace Spatial-Spectrum Estimator

A number of papers, including Refs.2 and 3 and dealing with wide-band signal-subspace spatial-spectrum estimation, have been widely accepted. However, they bring with them the problem of signal-subspace dimension expanding, which makes it difficult to make a correct division between signal-subspace and noise-subspace. We propose three different modified methods for overcoming signal-subspace dimension expanding. The first modified method, called by us the signal-subspace dimension estimation method, estimates signal-subspace dimension by a number of eigenvalues, which include 99.9% signal power; thus this method can make a correct division between the two subspaces. The other two modified methods do not even need to make a correct division between the two subspaces. Instead, these two methods, called by us weighted noise-subspace method and extended MUSIC method, reduce the effect of signal-subspace dimension expanding directly by using weighted eigenvectors in the noise-subspace or the entire space respectively. Computer simulation results show that all these modified methods overcome effectively the effect of signal-subspace dimension expanding of wide-band signal-subspace spatial-spectrum estimation and possess good statistical performance.