Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry

Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret, Hichem Snoussi

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

This paper proposes a new Bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non-identically Gaussian distribution. We introduce a prior enforcing a smooth
temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum
a posteriori estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The
proposed Bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason-2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms.
Original languageEnglish
Pages (from-to)2207-2219
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume54
Issue number4
Early online date24 Dec 2015
DOIs
Publication statusPublished - Apr 2016

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