Research Output per year

### 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.

Original language | English |
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Place of Publication | Birmingham |

Publisher | Aston University |

Number of pages | 8 |

ISBN (Print) | NCRG/98/021 |

Publication status | Published - 22 Oct 1998 |

### Keywords

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

## Fingerprint Dive into the research topics of 'A scatterometer neural network sensor model with input noise'. Together they form a unique fingerprint.

## Research Output

- 1 Article

## A scatterometer neural network sensor model with input noise

Cornford, D., Ramage, G. & Nabney, I. T., Jan 2000, In : Neurocomputing. 30, 1, p. 13-21 9 p.Research output: Contribution to journal › Article

## Cite this

Cornford, D., Ramage, G., & Nabney, I. T. (1998).

*A scatterometer neural network sensor model with input noise*. Aston University.