  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 sol
