@inproceedings{b2b01d19bad34d66973cba824adfbd16,
title = "Outlier Removal of Discontinuous Satellite Telemetry Data Based on Deconvolutional Reconstruction Network",
abstract = "Satellite telemetry data is important strategic resource. We can monitor and predict the status of the satellite through analyzing the telemetry data. However, due to interference on the satellite and sensor failure, the telemetry data will jump and generate outliers. Therefore, it is necessary to identify and remove the outliers. This paper proposes an outlier removal method based on deconvolutional reconstruction network. The deconvolutional reconstruction network is composed of multiple convolution and deconvolution which is used to learn the internal laws from massive telemetry data. The learned network can make accurate predictions for normal data except for outliers. Our method use this difference to set the threshold and perform outlier removal. The deconvolutional reconstruction network proposed in this paper uses a very few parameters for rapid learning. The network can converge within 20 epochs for multiple sets of telemetry datasets which contains more than 60k discontinuous points. Numerical experiments show that the proposed method can achieve perfect removal effects.",
keywords = "Deconvolutional Reconstruction Network, Outlier Removal, Satellite Telemetry Data",
author = "Haotian Zhao and Ming Liu and Tianyi Luo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 ; Conference date: 03-08-2022 Through 05-08-2022",
year = "2022",
doi = "10.1109/DDCLS55054.2022.9858392",
language = "英语",
series = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6--10",
editor = "Mingxuan Sun and Zengqiang Chen",
booktitle = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",
address = "美国",
}