Optimized Multi-Position Calibration Method with Nonlinear Scale Factor for Inertial Measurement Units

In Engineering Geodesy, most coordinate frames are aligned with the local vertical. For many measurement tasks, it is therefore necessary to manually (or arithmetically) align sensors or equipment with the local vertical, which is a common source of errors and it is very time consuming. Alternatively, accelerometer triads as part of inertial measurement units (IMUs) are used in several applications for horizon leveling. In this contribution we analyze and develop a method to use accelerometer triads for pole tilt compensation with total stations. Several triad sensor models are investigated and applied in a calibration routine using an industrial robot arm. Furthermore a calibration routine to determine the orientation of the IMU mounted on the pole is proposed. Using variance based sensitivity analysis we investigate the influence of different model parameters on leveling and pole tilt compensation. Based on this inference the developed calibration routines are adjusted. The final evaluation experiment shows an RMS of 2.4 mm for the tilt compensated measured ground point with tilts up to 50 gon.

Section: Improvement Strategysupporting

confidence: 90%

Accelerometer Triad Calibration for Pole Tilt Compensation Using Variance Based Sensitivity Analysis

Thalmann

Zechner

Neuner

2020

“…The single-axis RINS consists of a tri-axis fiber optic gyroscope, and tri-axis quartz accelerometer with precisions of 0.01 • /h and 0.01 mg respectively. Firstly, the thermal calibration was performed based on the traditional method and the two proposed methods, then the IMU calibration was done based on our previous work [29] to transform the pulse output into the rotation rate and specific force.…”

Section: Thermal Calibration and Verification Experimentsmentioning

confidence: 99%

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System

Wang

Cheng

Du³

2020

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

“…In essence, IMU calibration technology is the process of optimizing the calibration parameters in the calibration model, so the selection of the mathematical model will directly affect calibration result. In order to improve the calibration accuracy, the second-order nonlinear scale factor [16] is introduced into the calibration model in this paper:…”

Section: Calibration Model Of Triaxial Accelerometermentioning

confidence: 99%

“…Different from the research of Ye et al, in 2019, Wang et al developed a calibration method for the IMU with a nonlinear mathematical model. This method could get high calibration accuracy by Levenberg-Marquardt (LM) optimization algorithm, but the mathematical model which it used is too complicated [16]. To sum up, it can be found that the optimization algorithm, as a common method for processing data and optimizing indicators [17,18], is now widely used in sensor calibration.…”

Section: Introductionmentioning

confidence: 99%

In-Field Calibration of Triaxial Accelerometer Based on Beetle Swarm Antenna Search Algorithm

Wang

Gao

et al. 2020

Traditional calibration method is usually performed with expensive equipments such as three-axis turntable in a laboratory environment. However in practice, in order to ensure the accuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibrate the inertial measurement unit (IMU) without external equipment in the field. In this paper, a new in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search (BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithm and its improvements based on basic beetle antennae search (BAS) algorithm are introduced in detail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established for higher calibration accuracy, and then 24 optimal measurement positions are designed by theoretical analysis. In addition, the calibration procedures are improved according to the characteristics of BSAS algorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm. Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, which shows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate that the proposed method can achieve high precision in-field calibration without any external equipment, and meet the accuracy requirements of the INS.