Estimating exponential polynomial signals

Author

Golden, S. ; Friedlander, B.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Davis, CA, USA

Volume

2

fYear

1994

fDate

31 Oct-2 Nov 1994

Firstpage

811

Abstract

In this paper we approximate arbitrary complex signals by modeling both the logarithm of the amplitude and the phase of the complex signal as finite-order polynomials in time. We present a computationally efficient algorithm that estimates the unknown parameters by successively solving a series of optimization problems that are only a function of a single unknown complex parameter. At high signal-to-noise ratios, the mean-squared error of the estimates are shown to be close to the Cramer-Rao bound for a particular example by using a Monte Carlo simulation

Keywords

Gaussian noise; Monte Carlo methods; computational complexity; optimisation; parameter estimation; polynomials; signal representation; white noise; AWGN; Cramer-Rao bound; Monte Carlo simulation; Taylor expansion; arbitrary complex signals; computationally efficient algorithm; estimation; exponential polynomial signals; finite-order polynomials; mean-squared error; modeling; optimization problems; signal amplitude; signal phase; signal-to-noise ratio; Additive white noise; Finite difference methods; Maximum likelihood estimation; Phase estimation; Polynomials; Signal processing; Signal to noise ratio; Speech processing; Taylor series; Tin;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1994. 1994 Conference Record of the Twenty-Eighth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-6405-3

Type

conf

DOI

10.1109/ACSSC.1994.471574

Filename

471574