Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method

doi:10.3390/s23136030

	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)
刊名	SENSORS
	2023-07-01
卷号	23 期号:13 页码:6030
关键词	Noise2Noise deep learning denoising accelerometer inertial sensor
DOI	10.3390/s23136030
英文摘要	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.
分类号	二类
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
语种	英语
WOS记录号	WOS:001031132000001
资助机构	National Key Research and Development Program of China [2020YFC2200601, 2020YFC2200602, 2021YFC2201901]
其他责任者	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.
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/92546]
专题	力学研究所_国家微重力实验室
作者单位	1.{Xu, Peng, Luo, Ziren} Chinese Acad Sci, Inst Mech, Beijing 100094, Peoples R China 2.{Yang, Zhilan} Chinese Acad Sci, Natl Space Sci Ctr, Beijing 100094, Peoples R China 3.{Yang, Zhilan} Univ Chinese Acad Sci, Beijing 100094, Peoples R China 4.{Yang, Zhilan, Xu, Peng, Luo, Ziren} Hangzhou Inst Adv Study UCAS, Hangzhou 310000, Peoples R China 5.{Zhang, Haoyue, Xu, Peng} Lanzhou Univ, Lanzhou Ctr Theoret Phys, Lanzhou 730000, Peoples R China
推荐引用方式 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.
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.