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Onboard electrostatic suspension inertial sensors are important applicationsfor gravity satellites and space gravitational wave detection missions, and itis important to suppress noise in the measurement signal. Due to the complexcoupling between the working space environment and the satellite platform, theprocess of noise generation is extremely complex, and traditional noisemodeling and subtraction methods have certain limitations. With the developmentof deep learning, applying it to high-precision inertial sensors to improve thesignal-to-noise ratio is a practically meaningful task. Since there is a singlenoise sample and unknown true value in the measured data in orbit, odd-evensub-samplers and periodic sub-samplers are designed to process general signalsand periodic signals, and adds reconstruction layers consisting of fullyconnected layers to the model. Experimental analysis and comparison areconducted based on simulation data, GRACE-FO acceleration data and Taiji-1acceleration data. The results show that the deep learning method is superiorto traditional data smoothing processing sol

Unsupervised noise reductions for gravitational reference sensors or accelerometers based on Noise2Noise method