The Potential of Smartlnb Networks for Rainfall Estimation

摘要

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/N₀from 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/N₀, and then we present an adaptive tracking algorithm based on two Kalman filters: one that tracks slow changes in E_s/N₀due to external causes and another which tracks fast E_s/N₀variations due to rain. A comparison of the outputs of the two filters confirms the reliability of the rainfall rate estimate.