Title :
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
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.604633
