Blind restoration of linearly degraded discrete signals by Gibbssampling

Authors:
R. Chen;T.-H. Li
Affiliations:
Dept. of Stat., Texas A&M Univ., College Station, TX;-
Venue:
IEEE Transactions on Signal Processing
Year:
1995

Citing 0
Cited 6

A probabilistic inference of multiparty-conversation structure based on Markov-switching models of gaze patterns, head directions, and utterances

ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
A Bayesian framework for the development of suboptimal blind multiuser detectors for DS/CDMA signals in synchronous channels

Signal Processing
Particle filtering equalization method for a satellite communication channel

EURASIP Journal on Applied Signal Processing
A bayesian approach to blind separation of mixed discrete sources by gibbs sampling

UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Probabilistic inference of gaze patterns and structure of multiparty conversations from head directions and utterances

JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
A novel signal detection approach for fading channels with parameter statistics characteristics unknown

Digital Signal Processing

Quantified Score

Hi-index	35.68

Visualization

Abstract

This paper addresses the problem of simultaneous parameter estimation and restoration of discrete-valued signals that are blurred by an unknown FIR filter and contaminated by additive Gaussian white noise with unknown variance. Assuming that the signals are stationary Markov chains with known state space but unknown initial and transition probabilities, Bayesian inference of all unknown quantities is made from the blurred and noisy observations. A Monte Carlo procedure, called the Gibbs sampler, is employed to calculate the Bayesian estimates. Simulation results are presented to demonstrate the effectiveness of the method