| Sensor Head Temperature Distribution Reconstruction of High-Precision Gravitational Reference Sensors with Machine Learning | |
| Duan ZC(段宗超)1,2,3; Ren, Feilong4; Qiang, LiE2; Qi KQ(齐克奇)5; Zhang HY(张昊越)6 | |
| Corresponding Author | Qiang, Li-E([email protected]) |
| Source Publication | SENSORS
 |
| 2024-04-01 | |
| Volume | 24Issue:8Pages:26 |
| Abstract | 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. |
| Keyword | gravitational reference sensors temperature reconstruction simulation interpolation machine learning |
| DOI | 10.3390/s24082529 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001210058800001 |
| WOS Keyword | SPACE |
| WOS Research Area | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS Subject | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| Funding Project | National Key R&D Program of China |
| Funding Organization | National Key R&D Program of China |
| Classification | 二类 |
| Ranking | 3+ |
| Contributor | Qiang, Li-E |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/95041 |
| Collection | 微重力重点实验室 |
| Affiliation | 1.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Sch Fundamental Phys & Math Sci, Hangzhou 310024, Peoples R China; 2.Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100190, Peoples R China; 3.Univ Chinese Acad Sci, Taiji Lab Gravitat Wave Universe Beijing Hangzhou, Beijing 100049, Peoples R China; 4.Xian Aerosp Remote Sensing Data Technol Corp, Xian 710054, Peoples R China; 5.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; 6.Harbin Inst Technol, Res Ctr Satellite Technol, Harbin 150001, Peoples R China |
| Recommended Citation 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.Rp_Au:Qiang, Li-E |
| 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. |
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