A scatterometer neural network sensor model with input noise


The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
ISBN: NCRG/98/021
Last Modified: 29 Nov 2023 14:16
Date Deposited: 29 Jul 2009 11:17
PURE Output Type: Technical report
Published Date: 1998-10-22
Authors: Cornford, Dan
Ramage, Guillaume
Nabney, Ian T.


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