Parameter estimation of superimposed sinusoids by data matrix subfactorization: Analysis and results

Abstract

Estimating parameters of a sum of complex exponentials in white noise is considered in this paper. A simplified maximum likelihood estimation algorithm based on subfactorization of a structured data matrix is proposed, and we show that parameterization of the data model in signal space allows to improve estimation accuracy at low signal-to noise ratio (SNR). Basing on the proposed algorithm the computer simulation of the numerical example is accomplished. It is shown that the acheived accuracy is slightly less than the accuracy of efficient estimate corresponding to the low Cramer-Rao bound.