Statistical signal processing approach for rain estimation based on measurements from network management systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we apply statistical signal processing methodologies on a real-world application of using Commercial Microwave Links (CMLs) as opportunistic sensors for rain monitoring. We formulate an appropriate parameter estimation problem, taking advantage on the empirically evaluated statistics of the rain, and present a new methodology for rain estimation given only the quantized minimum and maximum radio signal level measurements, which are being logged regularly by the network management systems. Our method transforms measurements taken from any single CML, without the need for training series, nor any prior or side information, into rainfall estimates, that is - to a virtual rain gauge. The operation of the proposed method was demonstrated using actual CMLs in Israel in a semiarid climate zone, and shows that the achieved rain estimates agrees with near-by dedicated rain gauges.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9026-9030
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Applications of statistical signal processing techniques
  • Remote sensing of the environment

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