| Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method | |
Yang ZL(杨智岚); Zhang HY(张昊越); Xu P(徐鹏); Luo ZR(罗子人)
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| Source Publication | SENSORS
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| 2023-07-01 | |
| Volume | 23Issue:13Pages:6030 |
| Abstract | Onboard electrostatic suspension inertial sensors are important applications for gravity satellites and space gravitational-wave detection missions, and it is important to suppress noise in the measurement signal. Due to the complex coupling between the working space environment and the satellite platform, the process of noise generation is extremely complex, and traditional noise modeling and subtraction methods have certain limitations. With the development of deep learning, applying it to high-precision inertial sensors to improve the signal-to-noise ratio is a practically meaningful task. Since there is a single noise sample and unknown true value in the measured data in orbit, odd-even sub-samplers and periodic sub-samplers are designed to process general signals and periodic signals, and adds reconstruction layers consisting of fully connected layers to the model. Experimental analysis and comparison are conducted based on simulation data, GRACE-FO acceleration data, and Taiji-1 acceleration data. The results show that the deep learning method is superior to traditional data smoothing processing solutions. |
| Keyword | Noise2Noise deep learning denoising accelerometer inertial sensor |
| DOI | 10.3390/s23136030 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001031132000001 |
| WOS Research Area | Chemistry ; Engineering ; Instruments & Instrumentation |
| WOS Subject | Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation |
| Funding Organization | National Key Research and Development Program of China [2020YFC2200601, 2020YFC2200602, 2021YFC2201901] |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | Xu, P (corresponding author), Hangzhou Inst Adv Study UCAS, Hangzhou 310000, Peoples R China. ; Xu, P (corresponding author), Lanzhou Univ, Lanzhou Ctr Theoret Phys, Lanzhou 730000, Peoples R China. ; Xu, P (corresponding author), Chinese Acad Sci, Inst Mech, Beijing 100094, Peoples R China. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/92546 |
| Collection | 微重力重点实验室 |
| Affiliation | 1.{Yang, Zhilan} Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100094, Peoples R China 2.{Yang, Zhilan} Univ Chinese Acad Sci, Beijing 100094, Peoples R China 3.{Yang, Zhilan, Xu, Peng, Luo, Ziren} Hangzhou Inst Adv Study UCAS, Hangzhou 310000, Peoples R China 4.{Zhang, Haoyue, Xu, Peng} Lanzhou Univ, Lanzhou Ctr Theoret Phys, Lanzhou 730000, Peoples R China 5.{Xu, Peng, Luo, Ziren} Chinese Acad Sci, Inst Mech, Beijing 100094, Peoples R China |
| Recommended Citation GB/T 7714 | Yang ZL,Zhang HY,Xu P,et al. Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method[J]. SENSORS,2023,23,13,:6030.Rp_Au:Xu, P (corresponding author), Hangzhou Inst Adv Study UCAS, Hangzhou 310000, Peoples R China., Xu, P (corresponding author), Lanzhou Univ, Lanzhou Ctr Theoret Phys, Lanzhou 730000, Peoples R China., Xu, P (corresponding author), Chinese Acad Sci, Inst Mech, Beijing 100094, Peoples R China. |
| APA | 杨智岚,张昊越,徐鹏,&罗子人.(2023).Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method.SENSORS,23(13),6030. |
| MLA | 杨智岚,et al."Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method".SENSORS 23.13(2023):6030. |
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| Jp2023A237.pdf（3374KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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