基于遥测数据的卫星微异常检测

Chao Sun, Shaojun Chen, M. E, Ying Du, Chuanmin Ruan
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引用次数: 3

摘要

空间安全防御和空间快速反应的军事要求日益迫切。准确有效的在轨卫星微异常检测是卫星全生命周期健康管理的重要技术途径。在这种军事背景下,提出并开展了卫星关键部件的微异常检测。针对传统Voherra序列模型在卫星遥测信号微异常检测中诊断精度低的问题,提出了基于优化序列模型的遥测数据特征提取o-Voherra序列异常检测模型。首先,利用构建的优化序列模型提取卫星遥测数据的特征;其次,对遥测数据进行预处理和特征提取后的相空间重构。最后,利用所提出的o-Voherra序列模型实现了遥测数据的微异常检测。通过卫星关键部件脱敏后的遥感数据实验,所提出的模型能够准确实现卫星关键部件的微异常检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Satellite Micro Anomaly Detection Based on Telemetry Data
The military requirements of space security defense and space fast response are increasingly urgent. Accurate and effective micro anomaly detection of on-orbit satellites is an important technical way of satellites life cycle health management. Under this military background, the micro anomaly detection of the key components of the satellite is proposed and carried out. In order to solve the problems of low diagnostic accuracy of the traditional Voherra series model in satellite telemetry signal micro anomaly detection, o-Voherra series anomaly detection model for the feature extraction of telemetry data based on the optimized sequence model is proposed. Firstly, the feature of satellite telemetry data is extracted by using the constructed optimized sequence model. Secondly, phase space reconstruction of telemetry data after preprocessing and feature extraction. Finally, the telemetry data micro anomaly detection are realized by the proposed o-Voherra series model. Through the remote sensing data experiment of the key components of the satellite after desensitization, the proposed model can accurately realize the micro anomaly detection of the key components of the satellite.
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