A novel technique for broadband SVD
John G. McWhirter, Paul David Baxter · 2004
The singular value decomposition (SVD) is a very important tool for narrowband adaptive sensor array processing. It finds application in areas as diverse as high resolution direction finding, stabilised adaptive beamforming and blind signal separation [1,2]. The SVD decorrelates the signals received from an array of sensors by applying a unitary matrix of complex scalars which serves 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 identified and separated.