@techreport{d10d6310e69b4c218b1ac1132dd5bb4e,
title = "A scatterometer neural network sensor model with input noise",
abstract = "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.",
keywords = "non-linear regression, input uncertainty, wind retrieval, scatterometer",
author = "Dan Cornford and Guillaume Ramage and Nabney, {Ian T.}",
year = "1998",
month = oct,
day = "22",
language = "English",
isbn = "NCRG/98/021",
publisher = "Aston University",
type = "WorkingPaper",
institution = "Aston University",
}