如何确定最优噪声子空间？
How to Determine an Optimal Noise Subspace?
摘要：
基于信号子空间和噪声子空间正交性的多信号分类（MUSIC）算法是波达方向（DOA）估计中最常用的方法之一，其DOA估计性能主要取决于噪声子空间的准确性。在现有的研究中，噪声子空间是由阵列输出协方差矩阵的所有小特征值所对应的特征向量构成的。然而，我们发现，传统的编队中通过噪声子空间进行DOA估计并不是在所有情况下都是最优的，而使用部分噪声子空间总是可以获得最优的估计结果。换句话说，由对应于小特征值的一部分的特征向量所张成的子空间更能代表噪声子空间。我们通过大量的实验证明了这一结论。因此，应该选择哪些特征向量以及多少特征向量来形成部分噪声子空间似乎是一个有趣的问题。此外，本研究提出一个更普遍的问题：如何选择特征向量，以确定一个最佳的噪声子空间？
英文摘要：
The Multiple Signal Classification (MUSIC) algorithm based on the orthogonality between the signal subspace and noise subspace is one of the most frequently used method in the estimation of Direction Of Arrival (DOA), and its performance of DOA estimation mainly depends on the accuracy of the noise subspace. In the most existing researches, the noise subspace is formed by (defined as) the eigenvectors corresponding to all small eigenvalues of the array output covariance matrix. However, we found that the estimation of DOA through the noise subspace in the traditional formation is not optimal in almost all cases, and using a partial noise subspace can always obtain optimal estimation results. In other words, the subspace spanned by the eigenvectors corresponding to a part of the small eigenvalues is more representative of the noise subspace. We demonstrate this conclusion through a number of experiments. Thus, it seems that which and how many eigenvectors should be selected to form the partial noise subspace would be an interesting issue. In addition, this research poses a much general problem: how to select eigenvectors to determine an optimal noise subspace?
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
