A Novel Technique for Broadband Singular Value Decomposition
McWhirter, John, Baxter, Paul D. · 2004
Abstract The singular value decomposition (SVD) is a very important tool for narrowband adaptive sensor array processing. The SVD decorrelates the signals received from an array of sensors by applying a unitary matrix of complex scalars, which serve to modify the signals in phase and amplitude. Because the transformation is unitary, the associated singular values represent the true energy associated with each of the decorrelated components so the signal and noise subspaces may sometimes be separated. In broadband applications, or a situation in which narrowband signals have been convolutively mixed, the received signals cannot be represented in terms of phase and amplitude. Instantaneous decorrelation using a unitary matrix is no longer sufficient to separate them. It is necessary to impose decorrelation, not just at the same time instant for all signals, but over a suitably chosen range of relative time delays. This is referred to as strong decorrelation. Implementing strong decorrelation involves the application of a matrix of suitably chosen FIR filters and if each filter is represented in terms of its z-transform,