RESUMEN
In the climate change scenario the world is facing, extreme weather events can lead to increasingly serious disasters. To improve managing the consequent risks, there is a pressing need to have real-time systems that provide accurate monitoring and possibly forecasting which could help to warn people in the affected areas ahead of time and save them from hazards. The oblique earth-space links (OELs) have been used recently as a method for real-time rainfall detection. This technique poses two main issues related to its indirect nature. The first one is the classification of rainy and non-rainy periods. The second one is the determination of the attenuation baseline, which is an essential reference for estimating rainfall intensity along the link. This work focuses mainly on the first issue. Data referring to eighteen rain events were used and have been collected by analyzing a satellite-to-earth link quality and employing a tipping bucket rain gauge (TBRG) properly positioned, used as reference. It reports a comparison among the results obtained by applying four different machine learning (ML) classifiers, namely the support vector machine (SVM), neural network (NN), random forest (RF), and decision tree (DT). Various data arrangements were explored, using a preprocessed version of the TBRG data, and extracting two different sets of characteristics from the microwave link data, containing 6 or 12 different features, respectively. The achieved results demonstrate that the NN classifier has outperformed the other classifiers.
RESUMEN
The radar shadow effect prevents reliable target discrimination when a target lies in the shadow region of another target. In this paper, we address this issue in the case of Frequency-Modulated Continuous-Wave (FMCW) radars, which are low-cost and small-sized devices with an increasing number of applications. We propose a novel method based on Convolutional Neural Networks that take as input the spectrograms obtained after a Short-Time Fourier Transform (STFT) analysis of the radar-received signal. The method discerns whether a target is or is not in the shadow region of another target. The proposed method achieves test accuracy of 92% with a standard deviation of 2.86%.