Joint Model Selection and Parameter Estimation of GTD Model using RJ-MCMC Algorithm

Author

Zhiguang, Shi ; Jianxiong, Zhou ; Hongzhong, Zhao ; Qiang, Fu

Author_Institution

ATR Lab., National Univ. of Defence Technol., Changsha

Volume

3

fYear

2007

fDate

15-20 April 2007

Abstract

The Bayes principle is applied to the joint model selection and parameter estimation of GTD model to explore the prior information. An algorithm using RJ-MCMC is designed. It not only has better model selection and parameter estimation performance than the non-Bayes algorithms, but also solves the mixed parameter estimation problem in GTD model effectively. The advantage of this algorithm is especially evident at low SNR, for short data and with closely-spaced components. Simulations verify the effectiveness of this algorithm.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; geometrical theory of diffraction; parameter estimation; radar signal processing; Bayes principle; RJ-MCMC algorithm; joint model selection; parameter estimation; reversible jump Markov chain Monte Carlo solutions; Electromagnetic modeling; Electromagnetic scattering; Frequency; Laser radar; Optical scattering; Parameter estimation; Radar scattering; Scattering parameters; Signal processing algorithms; Solid modeling; Bayes principle; GTD model; RJ-MCMC; model order selection; parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on

Conference_Location

Honolulu, HI

ISSN

1520-6149

Print_ISBN

1-4244-0727-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2007.366795

Filename

4217825