{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T21:46:51Z","timestamp":1783374411275,"version":"3.54.6"},"reference-count":20,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFC2200601"],"award-info":[{"award-number":["2020YFC2200601"]}]},{"name":"National Key Research and Development Program of China","award":["2020YFC2200602"],"award-info":[{"award-number":["2020YFC2200602"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFC2201901"],"award-info":[{"award-number":["2021YFC2201901"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013even 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.<\/jats:p>","DOI":"10.3390\/s23136030","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T01:14:12Z","timestamp":1688087652000},"page":"6030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised Noise Reductions for Gravitational Reference Sensors or Accelerometers Based on the Noise2Noise Method"],"prefix":"10.3390","volume":"23","author":[{"given":"Zhilan","family":"Yang","sequence":"first","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hangzhou Institute for Advanced Study (UCAS), Hangzhou 310000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Lanzhou Center of Theoretical Physics, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3543-7777","authenticated-orcid":false,"given":"Peng","family":"Xu","sequence":"additional","affiliation":[{"name":"Hangzhou Institute for Advanced Study (UCAS), Hangzhou 310000, China"},{"name":"Lanzhou Center of Theoretical Physics, Lanzhou University, Lanzhou 730000, China"},{"name":"Institute of Mechanics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziren","family":"Luo","sequence":"additional","affiliation":[{"name":"Hangzhou Institute for Advanced Study (UCAS), Hangzhou 310000, China"},{"name":"Institute of Mechanics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1126\/science.1099192","article-title":"GRACE measurements of mass variability in the Earth system","volume":"305","author":"Tapley","year":"2004","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.asr.2019.05.021","article-title":"GRACE accelerometer data transplant","volume":"64","author":"Bandikova","year":"2019","journal-title":"Adv. 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