Table of contents
About this book
This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering.
This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.
Data Mining Machine Learning LEO satellites Telemetry Data Processing Satellite Monitoring Aerospace Engineering
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-030-20212-5
- Copyright Information Springer Nature Switzerland AG 2020
- Publisher Name Springer, Cham
- eBook Packages Intelligent Technologies and Robotics
- Print ISBN 978-3-030-20211-8
- Online ISBN 978-3-030-20212-5
- Series Print ISSN 1860-949X
- Series Online ISSN 1860-9503
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