Filtering smooth altimetric signals using a Bayesian algorithm
Résumé
This paper presents a new Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The Bayesian model proposed takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation strategy.
Mots clés
smooth altimetric signals filtering
Bayesian algorithm
Bayesian strategy
Bayesian model
Gaussian properties
gamma Markov random
posterior distribution
parameter estimation strategy
altimetric parameters
Signal processing algorithms
Bayes methods
Logic gates
Satellites
Estimation
Correlation
Altimetry
coordinate descent algorithm
gamma Markov random fields
Bayesian inference
filtering theory
Gaussian noise
Markov processes
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