The Use of Linear Feature Projection for Precipitation Classification Using Measurements from Commercial Microwave Links

High frequency electromagnetic waves are highly influenced by atmospheric conditions, namely wireless microwave links with carrier frequency of tens of GHz can be used for precipitation monitoring. In the scope of this paper we present a novel detection/classification system capable of detecting wet periods, with the ability to classify the precipitation type as rain or sleet, given an attenuation signal from spatially distributed wireless commercial microwave links. Fade (attenuation) dynamics was selected as a

discriminating feature

providing the data for classification. Linear Feature Extraction method is formulated; thereafter, the efficiency is evaluated based on real data. The detection/classification system is based on the Fisher’s

linear discriminant

and

likelihood ratio test

. Its performance is demonstrated using actual Received Signal Level measurements from a cellular backhaul network in the northern part of Israel. In particular, the use of the raw data as well as its derivatives to achieve better classification performance is suggested.