NEFOCAST is a research project that aims at retrieving rainfall fields from channel attenuation measurements on satellite links. Rainfall estimation algorithms rely on the deviation of the measured E s /N 0 from the clear-sky conditions. Unfortunately, clear-sky measurements exhibit signal fluctuations (due to a variety of causes) which could generate false rain detections and reduce estimation accuracy. In this paper we first review the main causes of random amplitude fluctuations in the received E s /N 0 , and then we present an adaptive tracking algorithm based on two Kalman filters: one that tracks slow changes in E s /N 0 due to external causes and another which tracks fast E s /N 0 variations due to rain. A comparison of the outputs of the two filters confirms the reliability of the rainfall rate estimate.

The Potential of Smartlnb Networks for Rainfall Estimation / Luca, Facheris; Giannetti, F.; Moretti, M.; Reggiannini, R.; Petrolino, A.; Bacci, G.; Adirosi, E.; Baldini, L.; Melani, S.; Ortolani, A.. - ELETTRONICO. - (2018), pp. 120-124. ((Intervento presentato al convegno 20th IEEE Statistical Signal Processing Workshop, SSP 2018 tenutosi a Friburgo, Germania nel 2018 [10.1109/SSP.2018.8450692].

The Potential of Smartlnb Networks for Rainfall Estimation

Luca, Facheris;
2018

Abstract

NEFOCAST is a research project that aims at retrieving rainfall fields from channel attenuation measurements on satellite links. Rainfall estimation algorithms rely on the deviation of the measured E s /N 0 from the clear-sky conditions. Unfortunately, clear-sky measurements exhibit signal fluctuations (due to a variety of causes) which could generate false rain detections and reduce estimation accuracy. In this paper we first review the main causes of random amplitude fluctuations in the received E s /N 0 , and then we present an adaptive tracking algorithm based on two Kalman filters: one that tracks slow changes in E s /N 0 due to external causes and another which tracks fast E s /N 0 variations due to rain. A comparison of the outputs of the two filters confirms the reliability of the rainfall rate estimate.
2018 IEEE Statistical Signal Processing Workshop, SSP 2018
20th IEEE Statistical Signal Processing Workshop, SSP 2018
Friburgo, Germania
2018
Luca, Facheris; Giannetti, F.; Moretti, M.; Reggiannini, R.; Petrolino, A.; Bacci, G.; Adirosi, E.; Baldini, L.; Melani, S.; Ortolani, A.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1137621
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 1
social impact