Attitude anti-interference federal filtering algorithm for MEMS-SINS/GPS/magnetometer/SV integrated navigation system

Hongmei Zhang,Guangyan Xu,Xin Liu,Hao Liu,Huaqing Zhang

doi:10.1177/0020294019882965

Hongmei Zhang, Guangyan Xu + Show 3 more

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https://doi.org/10.1177/0020294019882965

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Journal: Measurement and Control	Publication Date: Jan 1, 2020
Citations: 10	License type: CC BY 4.0

Affiliation: Shenyang Aerospace University

Abstract

The attitude measurement systems composed of magnetic, angular rate and gravity sensors in the navigation system of an unmanned aerial vehicle are vulnerable to disturbed magnetic field observations. In this paper, an anti-interference integrated navigation federal Kalman filter is designed with fusing the information of microelectromechanical-system-based strapdown inertial navigation system, gravity field, geomagnetic field and solar vector (direction of sun). The filter works in the mode of fusion reset or no reset according to environmental conditions or manual setting. In addition, the characteristic is discovered that disturbed magnetic field observations can interfere with the state estimation covariance matrixes of quaternions, which in turn affect the fusion update of estimated quaternions in no reset mode of the federal filter. In order to eliminate the effect of magnetic field observations on the attitude information of pitch and roll angles, a method of correction update for a yaw angle component in quaternions using the magnetometer is designed, and an Euler angle information fusion method is developed by constructing a pseudo-covariance matrix of Euler angels in fusion update of the filter. Thus, when there exist disturbed magnetic field observations and the filter is in no reset mode, the estimated pitch and roll angles of the main filter after fusion update are not affected, and the yaw angle estimation errors of the main filter decrease relative to the estimation errors of the sub-filter which is corrected by a magnetometer. The effectiveness of the algorithm is illustrated by a numerical simulation and a semi-physical simulation.

Highlights

For small and medium-sized unmanned aerial vehicles (UAVs), improving the accuracy and stability of a navigation system using relatively low-cost sensors is one of the crucial issues to be considered in the design of an integrated navigation system
Considering that the attitude information obtained from atmospheric polarization modes measured by multiple polarization sensors is relived from the interference of body vibration and disturbing magnetic field, this paper proposes a federal Kalman filter that fuses microelectromechanical system (MEMS)-SINS, global positioning system (GPS), magnetometer and solar vector (SV) to estimate the state of UAV
In order to eliminate the effect of disturbed magnetic field observations on the attitude information of pitch and roll angles, we propose a method of correction update for yaw angle component in quaternions using the magnetometer

Summary

Introduction

For small and medium-sized unmanned aerial vehicles (UAVs), improving the accuracy and stability of a navigation system using relatively low-cost sensors is one of the crucial issues to be considered in the design of an integrated navigation system. Considering that the attitude information obtained from atmospheric polarization modes measured by multiple polarization sensors is relived from the interference of body vibration and disturbing magnetic field, this paper proposes a federal Kalman filter that fuses MEMS-SINS, GPS, magnetometer and SV to estimate the state of UAV.

Results

Conclusion