Real-Time Machine Learning Enabled Low-Cost Magnetometer System

Autor:	Siddique, Talha, Mahmud, Md. Shaad
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Geomagnetically Induced Currents (GICs) are one of the most hazardous effects of space weather. The rate of change in ground horizontal magnetic component dBH/dt is used as a proxy measure for GIC. In order to monitor and predict dBH/dt, ground-based fluxgate magnetometers are used. However, baseline correction is crucial before such magnetometer data can be utilized. In this paper, a low-cost Machine Learning (ML) enabled magnetometer system has been implemented to perform realtime baseline correction of magnetometer data. The predicted geomagnetic components are then used to derive a forecast for dBH/dt. Two different ML models were deployed, and their real-time and offline prediction accuracy were examined. The localized peaks of the predicted dBH/dt are further validated using binary event analysis. Comment: Selected in IEEE Sensors Conference 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.08065 Zobrazit plný text záznamu View this record from Arxiv