A Three-Stage Accelerometer Self-Calibration Technique for Space-Stable Inertial Navigation Systems

Abstract

As a specific force sensor, the tri-axis accelerometer is one of the core instruments in an inertial navigation system (INS). During navigation, its measurement error directly induces constant or alternating navigation errors of the same order of magnitude. Moreover, it also affects the estimation accuracy of gyro drift coefficients during the initial alignment and calibration, which will indirectly result in navigation errors accumulating over time. Calibration can effectively improve measurement accuracy of the accelerometer. Device-level calibration can identify all of the parameters in the error model, and the system-level calibration can accurately estimate part of these parameters. Combining the advantages of both the methods and making full use of the precise angulation of the space-stabilized platform, this paper proposes a three-stage accelerometer self-calibration technique that can be implemented directly in the space-stable INS. The device-level calibration is divided into two steps considering the large amount of parameters. The first step is coarse calibration, which identifies parameters except for the nonlinear terms, and the second step is fine calibration, which not only identifies the nonlinear parameters, but also improves the accuracy of the parameters identified in the first step. The follow-on system-level calibration is carried out on part of the parameters using specific force error and attitude error to further improve the calibration accuracy. Simulation result shows that by using the proposed three-stage calibration technique in the space-stable INS, the estimation accuracy of accelerometer error can reach 1×10−6 normalg order of magnitude. Experiment results show that after the three-stage calibration, the accuracy of latitude, longitude, and attitude angles has increased by over 45% and the accuracy of velocity has increased by over 22% during navigation.

High-Altitude Balloon-Based Sensor System Design and Implementation

“…These data are also valuable to analyze the raising processing and landing processing of the balloon. At the same time, the balloon speed, attitude, and other information are also worthy of analysis, so the attitude navigation system is in demand [11]. The inertial navigation sensor (INS) designed in this paper can provide both orientation and navigation data, which includes an IMU and runs an onboard enhanced Extended Kalman Filter (EKF).…”

High Precision Tri-Axial Quartz Flexible Accelerometers Resolution Measurement Method Based on Tri-Axial Turntable

“…, (15) and (16) are x-axis measurement requirement, y-axis measurement requirement and z-axis measurement requirement respectively. Analyzing (14), (15) and (16), it is obvious that x-axis and y-axis can be measured at the same time, x-axis and z-axis as well as y-axis and z-axis cannot be measured together. So at most, the resolution of two axes can be measured at one time, and these two axes are x-axis and y-axis.…”

“…Guan et al made use of a dual-turntable centrifuge as a device that provides standard acceleration reference to calibrate the lateral sensitivity of the low-frequency accelerometer [15]. Wu et al took advantages of the precise angle of the space-stabilized platform and realized the self-calibration of a three-stage accelerometer in spatially stable INS [16]. Yu et al proposed a method for continuously adjusting the accelerometer's scale factor and installation error to reduce motion sensitivity.…”

A Field Calibration Method for Low-Cost MEMS Accelerometer Based on the Generalized Nonlinear Least Square Method

“…During navigation, its measurement errors directly cause navigation errors of the same order of magnitude. Moreover, its measurement errors also affect the calibration of the gyroscope and initial alignment, which indirectly cause navigation errors [1]. The accelerometer errors are divided into two categories: Deterministic errors and stochastic errors [2].…”

“…The desired deterministic errors in a vector form are represented as in Eq. (1) where b x , b y , and b z are bias along x-axis, y-axis, and z-axis. s x , s y , and s z are scale-factors along x-axis, y-axis, and z-axis.…”