SUBSPACE-BASED SIGNAL ANALYSIS USING SINGULAR-VALUE DECOMPOSITION

In this paper, we present a unified approach to the (related) problems of recovering signal parameters from noisy observations and the ident ification of linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Bo th known and new SVD-based identification methods are classified in a subspace-oriented scheme. The singular value decomposition of a matrix constructed from the observed signal data provides the key step to a robust discrimination between desired signals and disturbing signals i n terms of signal and noise subspaces. The methods that are presented are contrasted by the way in which the subspaces are determined and ho w the signal or system model parameters are extracted from these subsp aces. Typical examples such as the direction-of-arrival problem and sy stem identification from input/output measurements are elaborated upon , and some extensions to time-varying systems are given.