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An atomic gravimeter dynamic measurement method based on Kalman filter

Chunfu Huang, An Li, Fangjun Qin, Jie Fang, Xi Chen

2022Measurement Science and Technology26 citationsDOI

Abstract

Abstract The atomic gravimeter is an innovative quantum sensor featuring high precision, great sensitivity, and lasting stability. Currently, one research focus is on the combination of the atomic gravimeter with an accelerometer to implement dynamic measurements and gather gravity information in a real-time and highly precise manner. The Kalman filter framework was utilized with gravity and accelerometer drift as its states to observe the outputs of the atomic gravimeter and accelerometer, and the fusion of their data is realized. It can restrain the influence of dynamic vibration noise and obtain high precision gravity information in real time. Moreover, the accelerometer drift was estimated and compensated for, so as to enhance the lasting stability of the system. Laboratory static, swing platform and lake navigation tests were carried out to verify the effectiveness and feasibility of the proposed method. As revealed in the static test, the proposed method could effectively inhibit the effect of noise, and enhance the precision of gravity measurement. After making a compensation for drift, the noise coefficients of the accelerometer, including the bias stability ( B ), acceleration random walk ( K ), and rate ramp ( R ) decreased noticeably. The swing platform test further verified the applicability of the proposed method in dynamic conditions. As proved in the lake test, better results were obtained at a maximum velocity of 8.5 km h −1 . The gravity from the high-precision strap-down gravimeter on the same boat was taken as the reference, and processed to obtain the residual error of 2.03 ± 7.12 mGal, so that the proposed method was superior to the offline 300 s smooth filter. The proposed method offers a new approach for the study of atomic gravimeter dynamic measurement.

Topics & Concepts

GravimeterAccelerometerKalman filterAccelerationNoise (video)Control theory (sociology)Sensitivity (control systems)Computer scienceSwingAcousticsStability (learning theory)SimulationPhysicsElectronic engineeringEngineeringOpticsArtificial intelligenceClassical mechanicsControl (management)Machine learningImage (mathematics)InterferometryOperating systemGeophysics and Gravity MeasurementsInertial Sensor and NavigationGNSS positioning and interference
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