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 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.

30 pages and 6 figures

References in corpus (4)

• Modeling Envisat RA-2 waveforms in the coastal zone: Case study of calm water contamination
• Bayesian Estimation of Smooth Altimetric Parameters: Application to Conventional and Delay/Doppler Altimetry
• A revisit to the GNSS-R code range precision
• Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach

Cited by in corpus (5)

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• 3D Target Detection and Spectral Classification for Single-photon LiDAR Data
• Bayesian Filtering of Smooth Signals: Application to Altimetry