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Elementary Matrix Decomposition Algorithm for Symmetric Extension of Laurent Polynomial Matrices and Its Application in Construction of Symmetric M-Band Filter Banks

机译:基础矩阵分解算法的劳伦多伦多亚矩阵对称扩展及其在对称M波段滤波器库构建中的应用

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In this paper, we develop a novel and effective algorithm for the construction of perfect reconstruction filter banks (PRFBs) with linear phase. In the algorithm, the key step is the symmetric Laurent polynomial matrix extension (SLPME). There are two typical problems in the construction: (1) For a given symmetric finite lowpass filter a with the polyphase, to construct a PRFBs with linear phase such that its low-pass band of the analysis filter bank is a. (2) For a given dual pair of symmetric finite low-pass filters, to construct a PRFBs with linear phase such that its low-pass band of the analysis filter bank is a, while its low-pass band of the synthesis filter bank is b. In the paper, we first formulate the problems by the SLPME of the Laurent polynomial vector(s) associated to the given filter(s). Then we develop a symmetric elementary matrix decomposition algorithm based on Euclidean division in the ring of Laurent polynomials, which finally induces our SLPME algorithm.
机译:在本文中,我们利用线阶段开发了一种新颖有效的算法,用于建设完美重建滤波器组(PRFB)。在算法中,关键步骤是对称的Laurent多项式矩阵扩展(SLPME)。结构中有两个典型问题:(1)对于具有多相的给定对称的有限滤波器A,以构造具有线性相位的PRFB,使得其分析滤波器组的低通带是a。 (2)对于给定双对对称的低通滤波器,以构造具有线性阶段的PRFB,使得其分析滤波器的低通带是A,而其合成滤波器组的低通带是湾在纸质中,我们首先通过与给定滤波器相关联的劳伦多片向量的SLPME制定问题。然后,我们开发了一种基于Laurent多项式环中的欧几里德分区的对称基本矩阵分解算法,最终引起了我们的SLPME算法。

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