天地一体化信息网络 ›› 2022, Vol. 3 ›› Issue (2): 89-97.doi: 10.11959/j.issn.2096-8930.2022025

• 研究 • 上一篇    

基于深度不确定性估计网络的低轨卫星互联网故障预测方法

孙文宇, 张伟嘉, 王立民   

  1. 中国电科网络通信研究院,河北 石家庄 050081
  • 修回日期:2022-04-09 出版日期:2022-06-01 发布日期:2022-06-01
  • 作者简介:孙文宇(1996-),男,硕士,中国电科网络通信研究院工程师,主要研究方向为低轨卫星互联网、卫星通信运行控制、深度学习。
    张伟嘉(1994-),男,硕士,中国电科网络通信研究院工程师,主要研究方向为深度学习与机器学习、卫星通信系统。
    王立民(1976-),男,中国电科网络通信研究院高级工程师,主要研究方向为卫星有效载荷、低轨卫星互联网。

Fault Detection Method of Low-Orbit Satellite Internet Based on Deep Uncertainty Estimation Network

Wenyu SUN, Weijia ZHANG, Limin WANG   

  1. Network and Communication Research Institute of CETC, Shijiazhuang 050081,China
  • Revised:2022-04-09 Online:2022-06-01 Published:2022-06-01

摘要:

低轨卫星互联网技术具有创新性强、已有案例少且卫星数量众多、拓扑变化频繁、载荷类型繁杂等特点,其故障类别难以靠专家系统或者工程经验等方法遍历和训练。同时传统的基于单颗卫星、单一功能的故障检测和诊断方法,对于复杂多变条件下的不确定性故障难以进行预测。针对以上问题,提出一种基于深度不确定性估计网络的低轨卫星互联网星座故障预测算法模型,并基于地面星地联试获得的数据对所提方法进行验证。实验结果表明,提出的方法可以提高已知故障的检测准确率,尤其是对未知故障具有更准确的预测能力。

关键词: 低轨卫星互联网, 低轨卫星星座, 故障预测, 深度神经网络, 不确定性估计

Abstract:

Low-orbit satellite internet technology is a highly innovative direction of development with few existing cases.Low-orbit satellite internet systems are characterised by a large number of constituent satellites, frequent topology changes, and complex payload types.The fault categories of such complex systems are diffi cult to be completely mastered with methods such as expert systems or practical engineering experience.Moreover, traditional fault detection and diagnosis methods tailored for a single satellite and a single functionality are diffi cult to predict uncertain faults under sophisticated and transient environment conditions.For the above problems, a fault prediction model based on the deep uncertainty estimation network for low-orbit satellite internet was proposed, and was verifi ed used self-collected data from simulated joint satellite-ground tests.Experimental results showed that the proposed method could improved the detection accuracy of known types of faults and demonstrated particular eff ectiveness in predicting faults of unknown types.

Key words: low-orbit satellite internet, low-orbit satellite constellation, fault detection, deep neural network, uncertainty estimation

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