Threshold extension of SVD-based algorithms

Abstract

Threshold computation is essential in comparing the statistical performance of algorithms when estimating signal parameters. The authors show that it is possible to extend the threshold effect of singular-value-decomposition (SVD)-based signal-processing algorithms by using the Prony-Lanczos (P-L) method to lower values of signal-to-noise ratio. The procedure is comprised of two steps. In the first step, a nonparametric spectrum analysis or beamforming is used to yield a good starting point. This is followed in the second step by the (P-L) algorithm, which performs a local search, a procedure relatively insensitive to outliers. Simulation results, based on the angles between the estimated and true subspaces using the SVD-based algorithm and the P-L method, provide valuable insight.