Blind restoration of linearly degraded discrete signals by Gibbs sampling
- 1 January 1995
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 43 (10), 2410-2413
- https://doi.org/10.1109/78.469847
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 methodKeywords
This publication has 9 references indexed in Scilit:
- Blind Deconvolution of Linear Systems with Multilevel Nonstationary InputsThe Annals of Statistics, 1995
- Blind identification and deconvolution of linear systems driven by binary random sequencesIEEE Transactions on Information Theory, 1992
- Blind equalization using a tricepstrum-based algorithmIEEE Transactions on Communications, 1991
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Identification of nonminimum phase systems using higher order statisticsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesIeee Transactions On Pattern Analysis and Machine Intelligence, 1984
- Deconvolution and Estimation of Transfer Function Phase and Coefficients for Nongaussian Linear ProcessesThe Annals of Statistics, 1982
- ON MINIMUM ENTROPY DECONVOLUTIONPublished by Elsevier BV ,1981
- Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communicationsIEEE Transactions on Automatic Control, 1980