TY - GEN
T1 - The use of linear feature projection for precipitation classification using measurements from commercial microwave links
AU - Cherkassky, Dani
AU - Ostrometzky, Jonatan
AU - Messer, Hagit
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Environmental monitoring
KW - Received Signal Level (RSL) measurements
KW - fade dynamics
KW - feature extraction
KW - rain sleet events classification/detection
UR - http://www.scopus.com/inward/record.url?scp=84857335845&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28551-6_63
DO - 10.1007/978-3-642-28551-6_63
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AN - SCOPUS:84857335845
SN - 9783642285509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 511
EP - 519
BT - Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
T2 - 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Y2 - 12 March 2012 through 15 March 2012
ER -