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 |
---|---|

Pages (from-to) | 13-21 |

Number of pages | 9 |

Journal | Neurocomputing |

Volume | 30 |

Issue number | 1 |

DOIs | |

Publication status | Published - Jan 2000 |

### 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 Technical report

## A scatterometer neural network sensor model with input noise

Cornford, D., Ramage, G. & Nabney, I. T., 22 Oct 1998, Birmingham: Aston University, 8 p.Research output: Preprint or Working paper › Technical report

File

## Cite this

Cornford, D., Ramage, G., & Nabney, I. T. (2000). A scatterometer neural network sensor model with input noise.

*Neurocomputing*,*30*(1), 13-21. https://doi.org/10.1016/S0925-2312(99)00137-X