Real-time Satellite Anomaly Data Tagging Based on DAE-LSTM

• Published in: International journal of advanced network, monitoring, and controls Vol. 8; no. 1; pp. 40 - 49
• Main Authors: Xia, Caiyuan, Yan, Qianshi
• Format: Journal Article
• Language: English
• Published: Xi'an Sciendo 01.01.2023; De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
• Subjects: Coding theory; Deep Learning; Explosions; LSTM Network; Methods; Neural networks; Satellite Data; Satellites; Self Coding Technology; Telemetry
• ISSN: 2470-8038; 2470-8038
• DOI: 10.2478/ijanmc-2023-0044

Spacecraft is the main carrier of human exploration of outer space, exploration and understanding of the Earth and the universe, and the development of spaceflight can promote human civilization andsocial development, and can meet the nee-ds of economic construction, scientific and technological development, security construction, social progress and other aspects. The current global number of satellites in orbit reaches 5,465, of which China has 541. The vigorous development of the space industry symbolizes the steady improvement of the country’s comprehensive national power and overall technology. During the operation, the satellite in orbit needs to transmit data to the ground, these data may be subject to interference from various aspects, or even equipment failure, we find these data in real time is very important to reduce losses. The data transmitted by satellite has obvious temporal characteristics, and Long Short-Term Memory (LSTM) network has obvious advantages for processing temporal data, so this paper proposes a BER marking model based on the combination of LSTM network and self-coding technology. By comparing the data before and after noise reduction, a threshold value can be determined, and the BERs can be accurately distinguished by this method. After testing with real satellite temperature data, the accuracy of the model detection reaches a high level.