中国光学（中英文）2025，Vol.18Issue(3)：641-649,9.DOI:10.37188/CO.2024-0177
基于回声状态网络的空间激光干涉低低跟踪重力卫星数据恢复方法研究
Echo state network-based data recovery method for low-low satellite-to-satellite tracking missions in laser interferometry
摘要
Abstract
As a follow-on mission to the GRACE low-low satellite-to-satellite tracking gravity mission,one of the twin satellites of laser ranging interferometer gravity mission GRACE Follow-On experienced an an-omaly in its accelerometer payload after one month of operation.This anomaly resulted in the loss of scientif-ic measurement data,a situation similar to the final phase of the GRACE.Therefore,research on accelero-meter data recovery is important to achieve the detection objectives of both GRACE and GRACE Follow-On.This paper proposes a novel method for accelerometer data recovery and reconstruction based on the Echo State Network in machine learning.By constructing a mapping relationship of accelerometer data bet-ween the twin satellites using the Echo State Network and improving the network performance through Bayesian optimization,this method can achieve high-precision and high-efficiency reconstruction of missing accelerometer data.Through experimental comparison with measured data,in the frequency band of gravity field detection,the prediction results in the along-track and radial directions have been shown to reach the level of 10-8 m·s-2/√Hz.In the cross-track direction,the results reach levels between 10-8 m·s-2/√Hz～10-7 m·s-2/√Hz.This reconstruction accuracy is comparable to,or even partially superior to,the official GRACE data transplant accuracy,making it preliminarily applicable to gravity field inversion.This research achieves high-precision accelerometer data recovery for low-low satellite-to-satellite tracking missions using machine learning methods.关键词
激光干涉测距重力卫星/低低跟踪/机器学习/数据处理Key words
laser ranging interferometer gravity satellite/low-low satellite-to-satellite tracking mission/ma-chine learning/data processing分类
信息技术与安全科学引用本文复制引用
江鸿,姚镇东,杨立伟,徐鹏,强丽娥..基于回声状态网络的空间激光干涉低低跟踪重力卫星数据恢复方法研究[J].中国光学（中英文）,2025,18(3):641-649,9.基金项目
国家重点研发计划(No.2020YFC2200603,No.2020YFC2200601) （No.2020YFC2200603,No.2020YFC2200601）
中国科学院重点部署科研专项(No.KGFZD-145-24-04-03) （No.KGFZD-145-24-04-03）
中国科学院国际伙伴计划(No.025GJHZ2023106GC) （No.025GJHZ2023106GC）
国家自然科学基金青年科学基金项目(No.11905017)Supported by National Key Research and Development Program of China(No.2020YFC2200603,No.2020YFC2200601) （No.11905017）
Chinese Academy of Sciences Key Deployment Research Program(No.KGFZD-145-24-04-03) （No.KGFZD-145-24-04-03）
The International Partnership Program of the Chinese Academy of Sciences(No.025GJHZ2023106GC) （No.025GJHZ2023106GC）
Project supported by the Young Scientists Fund of the National Natural Science Found-ation of China(No.11905017) （No.11905017）
