A scatterometer neural network sensor model with input noise

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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.

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Details

Original languageEnglish
Pages (from-to)13-21
Number of pages9
JournalNeurocomputing
Volume30
Issue1
DOIs
StatePublished - Jan 2000

Bibliographic note

See http://eprints.aston.ac.uk/1411/

    Keywords

  • non-linear regression, input uncertainty, wind retrieval, scatterometer

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Employable Graduates; Exploitable Research

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