Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Geoscience and Remote Sensing Year : 2016

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

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.
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Dates and versions

hal-01311346 , version 1 (04-05-2016)

Identifiers

Cite

Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret, Hichem Snoussi. Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry. IEEE Transactions on Geoscience and Remote Sensing, 2016, vol. 54 (n° 4), pp. 2207-2219. ⟨10.1109/TGRS.2015.2497583⟩. ⟨hal-01311346⟩
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