Polynomial Matrix Eigenvalue Decomposition Techniques for Multichannel Signal Processing (2018)
Abstract / truncated to 115 words
Polynomial eigenvalue decomposition (PEVD) is an extension of the eigenvalue decomposition (EVD) for para-Hermitian polynomial matrices, and it has been shown to be a powerful tool for broadband extensions of narrowband signal processing problems. In the context of broadband sensor arrays, the PEVD allows the para-Hermitian matrix that results from the calculation of a space-time covariance matrix of the convolutively mixed signals to be diagonalised. Once the matrix is diagonalised, not only can the correlation between different sensor signals be removed but the signal and noise subspaces can also be identified. This process is referred to as broadband subspace decomposition, and it plays a very important role in many areas that require signal separation techniques ... toggle 6 keywordspolynomial matrix decomposition – eigenvalue decomposition – spectral factorisation – mimo systems – broadband subspace decomposition – multichannel signal processing
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