An atomic gravimeter dynamic measurement method based on Kalman filter

Abstract

Atomic gravimeter is an innovative quantum sensor featuring high precision, great sensitivity, and lasting stability. Presently, one research focus is on the combination of the atomic gravimeter with 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 data fusion of them 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, 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 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 8.5km/h. 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±7.12mGal, so that the proposed method was superior to the offline 300s smooth filter. The proposed method offers a new approach for the study of atomic gravimeter dynamic measurement.

DFFRLS-FAUKF: accurate and reliable monorail longitudinal slope identification method

“…Therefore, estimator-based parameter estimation methods have gradually become of greater interest to researchers. In road slope estimation, the traditional Kalman filter (KF) algorithm can be used to denoise nonlinear data [19]; however, this filter can no longer fulfill the current demands of automatic driving because it considers only the effect of Gaussian white noise and exhibits poor denoising ability in complex environments. Instead, the extended Kalman filter (EKF) algorithm can be used to estimate the slope by constructing an approximately linear function [20]; however, when a nonlinear system is approximated to a linear system, the negligence of the higher-order terms trigger filter divergence [21], leading to degradation of the slope estimation accuracy.…”

Airborne Absolute Gravimetry With a Quantum Sensor, Comparison With Classical Technologies

“…Like superconducting gravimeters, it is used to continuously monitor gravity with high long‐term stability (Freier et al., 2016; Ménoret et al., 2018). It has also been demonstrated that such technology could be implemented on moving vehicles like spring gravimeters or forced balanced accelerometers (Bidel et al., 2018, 2020; Huang et al., 2022). Atom interferometry technology is finally also studied for the next generation of sensor for space gravimetry (Lévèque et al., 2021; Reguzzoni et al., 2021; Trimeche et al., 2019; Zahzam et al., 2022).…”

A vibration compensation optimization method for a mobile atomic gravimeter

“…Bidel et al [18,19] constructed a shipboard and airborne absolute gravity measurement system with the Raman mirror placed on an accelerometer, which realized absolute gravity measurement in milligals after vibration compensation in a dynamic environment. In China, shipboard and vehicle-mounted dynamic measurements have been performed by scholars from the Zhejiang University of Technology [20], Naval University of Engineering [21,22], and Huazhong University of Science and Technology [11].…”