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

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4 Author(s)
Shi Zhiguang ; ATR Lab, National University of Defence Technology, Changsha, P.R. China. 410073 ; Zhou Jianxiong ; Zhao Hongzhong ; Fu Qiang

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.

Published in:

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  (Volume:3 )

Date of Conference:

15-20 April 2007