Next Article in Journal
Rolling Mechanism of Launch Vehicle during the Prelaunch Phase in Sea Launch
Previous Article in Journal
Investigation of High-Speed Rubbing Behavior of GH4169 Superalloy with SiC/SiC Composites
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation

1
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
2
Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Aerospace 2024, 11(5), 398; https://doi.org/10.3390/aerospace11050398
Submission received: 6 March 2024 / Revised: 7 May 2024 / Accepted: 12 May 2024 / Published: 15 May 2024

Abstract

In satellite health management, anomalies are mostly resolved after an event and are rarely predicted in advance. Thus, trend prediction is critical for avoiding satellite faults, which may affect the accuracy and quality of satellite data and even greatly impact safety. However, it is difficult to predict satellite operation using a simple model because satellite systems are complex and telemetry data are copious, coupled, and intermittent. Therefore, this study proposes a model that combines an attention mechanism and bidirectional long short-term memory (attention-BiLSTM) with telemetry correlation to predict satellite behaviour. First, a high-dimensional K-nearest neighbour mutual information method is used to select the related telemetry variables from multiple variables of satellite telemetry data. Next, we propose a new BiLSTM model with an attention mechanism for telemetry prediction. The dataset used in this study was generated and transmitted from the FY3E meteorological satellite power system. The proposed method was compared with other methods using the same dataset used in the experiment to verify its superiority. The results confirmed that the proposed method outperformed the other methods owing to its prediction precision and superior accuracy, indicating its potential for application in intelligent satellite health management systems.
Keywords: operation prediction; satellite; attention-BiLSTM; HKNN-MI; correlation telemetry operation prediction; satellite; attention-BiLSTM; HKNN-MI; correlation telemetry

Share and Cite

MDPI and ACS Style

Peng, Y.; Jia, S.; Xie, L.; Shang, J. Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation. Aerospace 2024, 11, 398. https://doi.org/10.3390/aerospace11050398

AMA Style

Peng Y, Jia S, Xie L, Shang J. Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation. Aerospace. 2024; 11(5):398. https://doi.org/10.3390/aerospace11050398

Chicago/Turabian Style

Peng, Yi, Shuze Jia, Lizi Xie, and Jian Shang. 2024. "Accurate Satellite Operation Predictions Using Attention-BiLSTM Model with Telemetry Correlation" Aerospace 11, no. 5: 398. https://doi.org/10.3390/aerospace11050398

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop