Filtering Smooth Altimetric Signals Using a Bayesian Algorithm

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Abstract

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.
Original languageEnglish
Publication statusPublished - 2016
Event24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sep 2016
Conference number: 24

Conference

Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016
CountryHungary
CityBudapest
Period29/08/162/09/16

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    Halimi, A., Buller, G. S., McLaughlin, S., & Honeine, P. (2016). Filtering Smooth Altimetric Signals Using a Bayesian Algorithm. Paper presented at 24th European Signal Processing Conference 2016, Budapest, Hungary.