Sensor Head Temperature Distribution Reconstruction of High-Precision Gravitational Reference Sensors with Machine Learning
文献类型：期刊论文
| 作者 | Duan ZC(段宗超)4,5,6; Ren, Feilong3; Qiang, LiE5; Qi KQ(齐克奇)2; Zhang HY(张昊越)1 |
| 刊名 | SENSORS
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 24期号:8页码:26 |
| 关键词 | gravitational reference sensors temperature reconstruction simulation interpolation machine learning |
| DOI | 10.3390/s24082529 |
| 通讯作者 | Qiang, Li-E(qianglie@nssc.ac.cn) |
| 英文摘要 | Temperature fluctuations affect the performance of high-precision gravitational reference sensors. Due to the limited space and the complex interrelations among sensors, it is not feasible to directly measure the temperatures of sensor heads using temperature sensors. Hence, a high-accuracy interpolation method is essential for reconstructing the surface temperature of sensor heads. In this study, we utilized XGBoost-LSTM for sensor head temperature reconstruction, and we analyzed the performance of this method under two simulation scenarios: ground-based and on-orbit. The findings demonstrate that our method achieves a precision that is two orders of magnitude higher than that of conventional interpolation methods and one order of magnitude higher than that of a BP neural network. Additionally, it exhibits remarkable stability and robustness. The reconstruction accuracy of this method meets the requirements for the key payload temperature control precision specified by the Taiji Program, providing data support for subsequent tasks in thermal noise modeling and subtraction. |
| 分类号 | 二类 |
| WOS关键词 | SPACE |
| 资助项目 | National Key R&D Program of China |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001210058800001 |
| 资助机构 | National Key R&D Program of China |
| 其他责任者 | Qiang, Li-E |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/95040]  |
| 专题 | 力学研究所_国家微重力实验室 |
| 作者单位 | 1.Harbin Inst Technol, Res Ctr Satellite Technol, Harbin 150001, Peoples R China 2.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; 3.Xian Aerosp Remote Sensing Data Technol Corp, Xian 710054, Peoples R China; 4.Univ Chinese Acad Sci, Taiji Lab Gravitat Wave Universe Beijing Hangzhou, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China; 6.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Duan ZC,Ren, Feilong,Qiang, LiE,et al. Sensor Head Temperature Distribution Reconstruction of High-Precision Gravitational Reference Sensors with Machine Learning[J]. SENSORS,2024,24(8):26. |
| APA | 段宗超,Ren, Feilong,Qiang, LiE,齐克奇,&张昊越.(2024).Sensor Head Temperature Distribution Reconstruction of High-Precision Gravitational Reference Sensors with Machine Learning.SENSORS,24(8),26. |
| MLA | 段宗超,et al."Sensor Head Temperature Distribution Reconstruction of High-Precision Gravitational Reference Sensors with Machine Learning".SENSORS 24.8(2024):26. |
入库方式： OAI收割
来源：力学研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。