Analysis and calibration of the gyro bias caused by geomagnetic field in a dual-axis rotational inertial navigation system

Cai, Qingzhong; Yang, Ge; Song, Ningfang; Yin, Hong; Liu, Yiliang

doi:10.1088/0957-0233/27/10/105001

Cited by 28 publications

(12 citation statements)

References 20 publications

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“…Considering that the test data are small sample unequal interval data, the sample size was expanded at proper intervals through interpolation. The common interpolation methods include linear interpolation, Lagrange interpolation, spline interpolation [14,15]. In actual engineering, Lagrange twopoint interpolation, Lagrange three-point quadratic interpolation (parabolic interpolation) and least squares approximation should be selected if the number of measuring points and test accuracy need to be considered [16].…”

Section: Course Effect Errormentioning

confidence: 99%

Design and Software Implementation of a Navigation Accuracy Evaluation Based on Error Model Solution

Huang¹,

Yi²,

Zeng³

et al. 2019

I2M

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This paper attempts to improve the accuracy of navigation accuracy evaluation of inertial navigation system (INS). Firstly, the course effect error of the INS was analyzed in details. Then, the errors of inertial devices like gyro and accelerometer were modelled separately. Based on these models, the author simulated how the course effect error and the second-order errors related to the specific force (SF2Es) affect the speed error, position error and attitude error of the INS, under two trajectory conditions. Based on the solution of the models, a navigation accuracy evaluation software was developed, which can evaluate one or more INSs at the same time. The research findings lay the theoretical basis for INS navigation accuracy evaluation.

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Section: Course Effect Errormentioning

confidence: 99%

Design and Software Implementation of a Navigation Accuracy Evaluation Based on Error Model Solution

Huang¹,

Yi²,

Zeng³

et al. 2019

I2M

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show abstract

“…In order to make simulation experiments closer to reality, the error model of the inertial device should be established before the accurate calibration of the misalignment angles between systems. In general, the inertial device biases consists of Gaussian white noise, the random constant bias, and the first-order Markov process [ 20 ]. The white Gaussian noise describes the fast changing characteristics of the deviation.…”

Section: Dynamic Calibration Scheme Overall Designmentioning

confidence: 99%

A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Hua

Yan-hong

et al. 2018

Sensors

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Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1″ while using the proposed method.

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“…There are two main methods for constructing geomagnetic reference maps. One is based on the existing physical model of the geomagnetic field, and the other is constructing a gridded geomagnetic reference map based on measured geomagnetic data [8]. The existing International Geomagnetic Reference Magnetic Field (IGRF) and World Magnetic Model (WMM) describe the earth's primary magnetic field [9].…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Geomagnetic Reference Map Reconstruction Based on Dictionary Learning and Sparse Representation

Zhang

et al. 2020

IEEE Access

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Geomagnetic matching navigation plays a vital role in the navigation and guidance field, and the construction of geomagnetic reference maps plays a significant role in geomagnetic matching navigation. This paper addresses the problem of generating high-resolution geomagnetic reference maps with limited measured data. Most existing methods can hardly satisfy the accuracy requirements. We base our study on the theory of image super-resolution reconstruction and approach this problem from the perspective of dictionary learning and sparse representation. We propose a method of sparse dictionary initialization based on prior information from rectangular harmonic analysis, and then the K-SVD algorithm is applied to the dictionary training process to improve the performance of the sparse dictionary. Three components of the geomagnetic field are considered for multi-channel sparse representation to enhance the quality of the constructed maps. Experimental results show that our proposed method outperforms other geomagnetic reference map construction methods in the reconstructed precision as well as the robustness to noise.

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Analysis and calibration of the gyro bias caused by geomagnetic field in a dual-axis rotational inertial navigation system

Cited by 28 publications

References 20 publications

Design and Software Implementation of a Navigation Accuracy Evaluation Based on Error Model Solution

Design and Software Implementation of a Navigation Accuracy Evaluation Based on Error Model Solution

A Dynamic Calibration Method of Installation Misalignment Angles between Two Inertial Navigation Systems

Super-Resolution Geomagnetic Reference Map Reconstruction Based on Dictionary Learning and Sparse Representation

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