Bayesian estimation of the parameters of a polynomial phase signal using MCMC methods

Author

Theys, C?©line ; Vieira, Michelle ; Ferrari, Andr?©

Author_Institution

CNRS, Nice, France

Volume

5

fYear

1997

Firstpage

3553

Abstract

The aim of this paper is Bayesian estimation of the parameters of a polynomial phase signal. This problem, encountered in radar systems for example, is usually solved using a time-frequency analysis or phase-only algorithms. A Bayesian approach using Markov chain Monte Carlo (MCMC) methods for estimating a posteriori densities of the polynomial parameters is proposed. The main advantage of this approach is that it gives a direct estimation of all polynomial coefficients, contrary to previously developed algorithms

Keywords

Bayes methods; Markov processes; Monte Carlo methods; noise; parameter estimation; polynomials; probability; radar signal processing; Bayesian estimation; MCMC methods; Markov chain Monte Carlo methods; a posteriori densities estimation; noisy polynomial phase signal; parameter estimation; phase-only algorithms; polynomial coefficients; polynomial parameters; radar systems; time-frequency analysis; Bayesian methods; Gaussian noise; Monte Carlo methods; Parameter estimation; Phase estimation; Phase noise; Polynomials; Radar; Stochastic processes; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.604633

Filename

604633