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
- Gaussian properties
- Bayesian inference
- filtering theory
- Gaussian noise
- Markov processes
- smooth altimetric signals filtering
- Bayesian algorithm
- Bayesian strategy
- Bayesian model
- posterior distribution
- gamma Markov random
- gamma Markov random fields
- coordinate descent algorithm
- Altimetry
- Correlation
- Estimation
- Satellites
- Logic gates
- Bayes methods
- Signal processing algorithms
- altimetric parameters
- parameter estimation strategy
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |