Skip to Main content Skip to Navigation
Journal articles

Parameter estimation for peaky altimetric waveforms

Abstract : Much attention has been recently devoted to the analysis of coastal altimetric waveforms. When approaching the coast, altimetric waveforms are sometimes corrupted by peaks caused by high reflective areas inside the illuminated land surfaces or by the modification of the sea state close to the shoreline. This paper introduces a new parametric model for these peaky altimetric waveforms. This model assumes that the received altimetric waveform is the sum of a Brown echo and an asymmetric Gaussian peak. The asymmetric Gaussian peak is parameterized by a location, an amplitude, a width, and an asymmetry coefficient. A maximum-likelihood estimator is studied to estimate the Brown plus peak model parameters. The Cramér-Rao lower bounds of the model parameters are then derived providing minimum variances for any unbiased estimator, i.e., a reference in terms of estimation error. The performance of the proposed model and the resulting estimation strategy are evaluated via many simulations conducted on synthetic and real data. Results obtained in this paper show that the proposed model can be used to retrack efficiently standard oceanic Brown echoes as well as coastal echoes corrupted by symmetric or asymmetric Gaussian peaks. Thus, the Brown with Gaussian peak model is useful for analyzing altimetric easurements closer to the coast.
Complete list of metadata

Cited literature [26 references]  Display  Hide  Download
Contributor : Open Archive Toulouse Archive Ouverte (OATAO) Connect in order to contact the contributor
Submitted on : Wednesday, March 13, 2013 - 10:27:37 AM
Last modification on : Monday, July 4, 2022 - 9:22:25 AM
Long-term archiving on: : Friday, June 14, 2013 - 4:10:10 AM


Files produced by the author(s)



Abderrahim Halimi, Corinne Mailhes, Jean-Yves Tourneret, Pierre Thibault, François Boy. Parameter estimation for peaky altimetric waveforms. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2013, vol. 51, pp.1568-1577. ⟨10.1109/TGRS.2012.2205697⟩. ⟨hal-00800065⟩



Record views


Files downloads