Self-Calibration Method of the Bias of Space Electrostatic Accelerometer-华中科技大学-引力中心
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Self-Calibration Method of the Bias of Space Electrostatic Accelerometer

发布时间:2016-11-11          来源:办公室           浏览次数:

Shao-Bo Qu1Xiao-Mei Xia1Yan-Zheng Bai1Shu-Chao Wu1 and Ze-Bing Zhou1,a)

1 MOE Key Laboratory of Fundamental Quantities Measurement, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China

a) Author to whom correspondence should be addressed. Electronic mail:

(Received 19 May 2016; accepted 14 October 2016; published online 9 November 2016)


Abstract The high precision space electrostatic accelerometer is an instrument to measure the non-gravitational forces acting on a spacecraft. It is one of the key payloads for satellite gravity measurement and space fundamental physics experiments. The measurement error of the accelerometer directly affects the precision of gravity field recovery for the Earth. This paper analyzes the sources of the bias according to the operating principle and structural constitution of the space electrostatic accelerometer. Models of bias due to the asymmetry of the displacement sensing system, including the mechanical sensor head and the capacitance sensing circuit, and the asymmetry of the feedback control actuator circuit are described separately. According to the two models, a method of bias self-calibration by using only the accelerometer data is proposed, based on the feedback voltage data of the accelerometer before and after modulating the DC biasing voltage (Vb) applied on its test mass. Two types of accelerometer biases are evaluated separately using in-orbit measurement data of a space electrostatic accelerometer. Based on the preliminary analysis, the bias of the accelerometer onboard of an experiment satellite is evaluated to be around 10-4 m/s2, about 4 orders of magnitude greater than the noise limit. Finally, considering the two asymmetries, a comprehensive bias model is analyzed. A modified method to directly calibrate the accelerometer comprehensive bias is proposed.


Rev. Sci. Instrum. 87, 114502 (2016);

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