  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.
