TY - JOUR
T1 - UAV Attitude Angle Measurement Method Based on Magnetometer‐Satellite Positioning System
AU - Qu, Gaomin
AU - Zhou, Zhou
AU - Li, Jiguang
AU - Shao, Zhuang
AU - Dong, Yanfei
AU - Guo, An
AU - Shao, Pengyuan
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - To improve the measurement accuracy of unmanned aerial vehicles (UAVs), in this study, we proposed a method for measuring the attitude angle of a UAV by the combination of a magnetometer and satellite positioning system. Based on the measurement principle, we established a combined measurement error model, analyzed the root cause of the measurement error, and calibrated the magnetometer using the least‐squares algorithm. For handling external or system interference effects, we designed a compensation algorithm that does not depend on environmental information. For handling measurement errors, a “current” statistical model was established, and the continuous‐discrete Kalman filter algorithm was used to eliminate measurement errors. The test results showed that after using the compensation algorithm, the measurement error was reduced from 17.80% to 6.86% compared to the average absolute error of the local magnetic field intensity, and the absolute measurement error was reduced by 61.49%. The absolute measurement error was compared after using the compensation + filtering algorithm, and the error after compensation was further reduced by 42.36%. Hence, we proved the feasibility and effectiveness of our method.
AB - To improve the measurement accuracy of unmanned aerial vehicles (UAVs), in this study, we proposed a method for measuring the attitude angle of a UAV by the combination of a magnetometer and satellite positioning system. Based on the measurement principle, we established a combined measurement error model, analyzed the root cause of the measurement error, and calibrated the magnetometer using the least‐squares algorithm. For handling external or system interference effects, we designed a compensation algorithm that does not depend on environmental information. For handling measurement errors, a “current” statistical model was established, and the continuous‐discrete Kalman filter algorithm was used to eliminate measurement errors. The test results showed that after using the compensation algorithm, the measurement error was reduced from 17.80% to 6.86% compared to the average absolute error of the local magnetic field intensity, and the absolute measurement error was reduced by 61.49%. The absolute measurement error was compared after using the compensation + filtering algorithm, and the error after compensation was further reduced by 42.36%. Hence, we proved the feasibility and effectiveness of our method.
KW - compensation algorithm
KW - continuous‐discrete Kalman filter
KW - least squares algorithm
KW - UAV attitude angle measurement
KW - “current” statistical model
UR - http://www.scopus.com/inward/record.url?scp=85132146057&partnerID=8YFLogxK
U2 - 10.3390/app12125947
DO - 10.3390/app12125947
M3 - 文章
AN - SCOPUS:85132146057
SN - 2076-3417
VL - 12
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 12
M1 - 5947
ER -