Single Value Decomposition and Signal Processing: v. 2

This volume is an outgrowth of the 2nd International Workshop on SVD and Signal Processing which was held in Kingston, Rhode Island, 25-27 June, 1990. The singular value decomposition (SVD) has been applied to signal processing problems since the late 1970's, although it has been known in various forms for over 100 years. SVD filtering has been shown to give better results at lower signal-to-noise ratios than classical techniques based on linear filtering. This explains in part the recent interest in SVD techniques for signal processing. This book is a compilation of papers that examine in detail the singular decomposition of a matrix and its application to problems in signal processing. Algorithms and implementation architectures for computing the SVD are discussed, and analysis techniques for predicting and understanding the performance of SVD-based algorithms are given. The volume will provide both a stimulus for future research in this field as well as useful reference material for many years to come.

Table of Contents:
Parts: I. Survey Papers. The SVD and reduced-rank signal processing (L.L. Scharf). Parallel implementations of the SVD using implicit CORDIC arithmetic (J-M. Delosme). Neural networks for extracting pure/constrained/oriented principal components (S.Y. Kung et al.). Generalizations of the OSVD: structure, properties and applications (B. de Moor). Perturbation theory for the singular value decomposition (G.W. Stewart). II. Algorithms and Architectures.An accurate product SVD algorithm (A.W. Bojanczyk et al.). The hyperbolic singular value decomposition and applications (R. Onn et al.). Adaptive SVD algorithm with application to narrowband signal tracking (W. Ferzali, J.G. Proakis). Chebyshev acceleration techniques for solving slowly varying total least squares problems (S. van huffel). Combined Jacobi-type algorithms in signal processing (M. Moonan et al.). A modified non-symmetric Lanczos algorithm and applications (D. Boley, G. Golub). Parallel one sided Euler-Jacobi method for symmetric eigendecomposition and SVD (A.W. Bojanczyk et al.). Using UNITY to implement SVD on the connection machine (M. Kleyn, I. Chakravarty). A CORDIC processor array for the SVD of a complex matrix (J.R. Cavallaro, A.C. Elster). III. Analysis of SVD-Based Algorithms. Analytical performance prediction of subspace-based algorithms for DOA estimation (Fu Li, R.J. Vaccaro). Spatial smoothing and MUSIC: Further results (B.D. Rao, K.V.S. Hari). A performance analysis of adaptive algorithms in the presence of calibration errors (D.R. Farrier, D.J. Jeffries). Second order perturbation calculation of state space estimation (W.W.F. Pijnappel et al.). The threshold effect in signal processing algorithms which use an estimated subspace (D.W. Tufts et al.). IV. Applications to signal modeling and detection OSVD and QSVD in signal separation (D. Callaerts et al.). Enhanced sinusoidal and exponential data modeling (J.A. Cadzow, D.M. Wilkes). Enhancements to SVD-Based detection (J.H. Cozzens, M.J. Sousa).Resolution of closely spaced coherent plane waves via SVD (H. Krim et al.). Transient parameter estimation by an SVD-based Wigner distribution (M.F. Grifin, A.M. Finn). Signal/noise subspace decomposition for random transient detection (N.M. Marinovich, L.M. Roytman). Comparisons of truncated QR and SVD methods for AR spectral estimations (S.F. Hsieh et al.). Using singular value decomposition to recover periodic waveforms in noise and with residual carrier (B. Rice). Other Applications. SVD-based low-rank approximations of rational models (A-J. van der Veen, E.F. Deprettere). Computing the singular values and vectors of a Hankel operator (H. Ozbay). SVD analysis of probability matrices (J.A. Ramos). Fast matrix-vector multiplication using displacement rank approximation via an SVD (J.M. Speiser et al.). A new use of singular value decomposition in bioelectric imaging of the brain (D.J. Major, R.J. Sidman).