An extension of the MUSIC algorithm to broadband scenarios using a polynomial eigenvalue decomposition
An extension of the MUSIC algorithm to broadband scenarios using a polynomial eigenvalue decomposition
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使用多项式特征值分解将 MUSIC 算法扩展到宽带场景
DOI：
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发表时间：
2011
期刊：
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通讯作者：
S. Lambotharan
中科院分区：
文献类型：
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作者：
Mohamed A. Alrmah;Stephan Weiss;S. Lambotharan
多信号分类（MUSIC）算法的波达方向估计被定义为窄带场景。在本文中，一个概括的宽带情况下，提出了一个多项式的空时协方差矩阵的宽带系统的描述的基础上。多项式特征值分解用于确定该矩阵的仅噪声子空间，该子空间可以通过适当定义的宽带导向矢量来扫描。提出了两种宽带MUSIC算法版本，其解决了单独的到达角或与源活跃的频率范围相结合。这些方法的初步结果，并证明了一个显着的好处，独立的频率仓处理使用窄带MUSIC。
The multiple signal classification (MUSIC) algorithm for direction of arrival estimation is defined for narrowband scenarios. In this paper, a generalisation to the broadband case is presented, based on a description of broadband systems by polynomial space-time covariance matrices. A polynomial eigenvalue decomposition is used to determine the noise-only subspace of the this matrix, which can be scanned by appropriately defined broadband steering vectors. Two broadband MUSIC algorithm versions are presented, which resolve either angle of arrival alone or in combination with the frequency range over which sources are active. Initial results for these approaches are presented and demonstrate a significant benefit over independent frequency bin processing using narrowband MUSIC.