Classification and prediction of lower troposphere layers influence on rf propagation using artificial neural networks

Abstract

This paper describes the basic steps of a novel approach to weather classification by remote and local atmosphere sensing. Atmospheric data on the troposphere are gathered by 10 GHz radio links and a number of meteorological sensors. Classification is performed by artificial neural networks (ANN) and is crucial for further processing, because of the different RF propagation influences under a variety of weather conditions. Reasons for using ANN compared to other means of classification are discussed. Differences in the size and number of hidden layers of back-propagation networks used are discussed. Different learning sets of measured data and their construction are also evaluated. © 2009 Springer Berlin Heidelberg.