Polynomial subspace decomposition for broadband angle of arrival estimation

Mohamed A. Alrmah, J. Corr, A. Alzin, K. Thompson, Stephan Weiss
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引用次数: 12

Abstract

In this paper we study the impact of polynomial or broadband subspace decompositions on any subsequent processing, which here uses the example of a broadband angle of arrival estimation technique using a recently proposed polynomial MUSIC (P-MUSIC) algorithm. The subspace decompositions are performed by iterative polynomial EVDs, which differ in their approximations to diagonalise and spectrally majorise s apce-time covariance matrix.We here show that a better diagonalisation has a significant impact on the accuracy of defining broadband signal and noise subspaces, demonstrated by a much higher accuracy of the P-MUSIC spectrum.
宽带到达角估计的多项式子空间分解
在本文中,我们研究了多项式或宽带子空间分解对任何后续处理的影响,这里使用了使用最近提出的多项式MUSIC (P-MUSIC)算法的宽带到达角估计技术的示例。子空间分解是由迭代多项式evd进行的,它们在对角化和频谱最大化的近似上有所不同。我们在这里表明,更好的对角化对定义宽带信号和噪声子空间的准确性有显著影响,P-MUSIC频谱的精度要高得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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