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

TL;DR

This paper introduces a Bayesian method for smooth estimation of altimetric parameters, effectively handling noise and ensuring physical plausibility, validated on real satellite data with improved accuracy over existing methods.

Contribution

A novel Bayesian framework with a smoothness prior and a low-cost gradient descent algorithm for real-time altimetric parameter estimation.

Findings

Improved parameter estimation accuracy on real satellite data.

Effective noise modeling with non-Gaussian distributions.

Low computational cost suitable for real-time applications.

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 presents a low computational cost which is suitable for real time applications. The proposed Bayesian strategy and the corresponding estimation algorithm are validated on 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 the state-of-the-art estimation algorithms.