Discrete Features Enhancement Based Online Anomaly Detection for Satellite Telemetry Series
Author:
Affiliation:
1. School of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China
2. School of Electronics and Information Engineering, Harbin Institute of Technology (HIT), Harbin, China
Funder
National Natural Science Foundation of China
Chongqing Doctor’s “Direct Train” Research Program of Chongqing Science and Technology Commission
Youth Project of Science and Technology Research Program of Chongqing Education Commission of China
High-Level Introduction of Talent Research Startup Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10288594.pdf?arnumber=10288594
Reference47 articles.
1. An Anomaly Detection Method for Satellites Using Monte Carlo Dropout
2. Deep Learning-based Anomaly Detection in Cyber-physical Systems
3. Research on Intelligent Anomaly Detection Algorithm of Launch Vehicle Telemetry Data
4. Online anomaly detection with sparse Gaussian processes
5. Improved deep learning based telemetry data anomaly detection to enhance spacecraft operation reliability
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