Performance of ESPRIT for Estimating Mixtures of Complex Exponentials Modulated by Polynomials

High Resolution (HR) methods are known to provide accurate frequency estimates for discrete spectra. The Polynomial Amplitude Complex Exponentials (PACE) model, also called quasipolynomial model in the literature, was presented as the most general model tractable by HR methods. A subspacebased estimation scheme was recently proposed, derived from the classical ESPRIT algorithm. In this paper, we focus on the performance of this estimator. We first present some asymptotic expansions of the estimated parameters, obtained at the first order under the assumption of a high signal-to-noise ratio. Then the performance of the generalized ESPRIT algorithm for estimating the parameters of this model is analyzed in terms of bias and variance, and compared to the Cram

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Roland Badeau, Gaël Richard, Bertrand David
• Keywords: ESPRIT Algorithm, Generalized Esprit Algorithm, Model, TSP 2008