The calibration method for accelerometers in the redundant MEMS inertial navigation system

Abstract

Compared with the non-redundant inertial navigation system (INS), the redundant INS (RINS) has more error parameters and the system is more complicated. The calibration methods for non-redundant INS are unable to be adopted by RINS directly. Meanwhile, the inner lever arm effect of accelerometers is more severe in RINS, which is not supposed to be ignored in the error compensation. To solve the problems above, this paper proposes a novel calibration method for accelerometers in RINS. First, the paper analyses and models the bias, scale factor error, installation angle error and lever arm error. Based on the error model, two Kalman filters are designed to estimate the error parameters. The calibration is divided into two steps: the bias, scale factor error and installation angle error are calibrated by the static multi-position experiment first, and then the lever arm error is calibrated by the rotation experiment. Experiments prove that the proposed method can effectively calibrate the deterministic error of the accelerometers, that the estimation errors are controlled at 1 × 10 −4 level. Further, the paper studies and optimizes the turntable rotation scheme in the lever arm error calibration experiment, and proposes the design principles for the rotation scheme. Comparing with the casually designed scheme, the optimized procedure can improve the accuracy by almost an order of magnitude as well as the time-consumption being shortened by 40%. The design principles are applicable to the other inertial navigation systems to improve the calibration accuracy and reduces the time cost.

A method for improving the performance of centering rod surveying based on two-position correction

“…Luczak [28] provides some guidelines for tilt measurement with MEMS accelerometers. Other methods for accelerometer calibration have also been proposed, such as Levenberg-Marquardt and the thin-shell algorithm [29], auto calibration [30], temperature compensation [31], scanning laser Doppler vibrometry [32], a gravity vector observation-based method [33] and a KF-based method [34]. However, center rod surveying is carried out in the field, and both operability and measurement accuracy need to be considered.…”

Development of IOT-based low-cost MEMS pressure sensor for groundwater level monitoring

“…It is an adjustment between the sensor's output signal and the measured quantity to reach the closest value to the truth [75,76]. Adopting a proper calibration model can significantly affect the performance of the MEMS-based instruments [32,[76][77][78][79][80]. A MEMS pressure sensor mainly suffer from four types of error, including offset, gain, nonlinearity, and hysteresis.…”

Design of a turntable for torque and rotation angle calibration and its angle calibration based on autocollimator

“…A series of harmonics can be used to represent the angle measurement error, find out the harmonic components in which the amplitude and initial phase are basically unchanged, and establish the error harmonic compensation model to reduce or even eliminate these systematic errors. Therefore, a large part of the residual error is the error introduced by factors such as the shaft runout of the turntable [9] .…”