# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 7 Conclusions  
In this study, we focus on the problems of external interference and measurement errors in the measurement of the UAV attitude angle. A combined measurement model is established based on the magnetometer-satellite positioning system combined with the measurement attitude angle program. According to the model, the source of the measurement error is analyzed, and the effects of external or system interference, measurement error, system error, and algorithm error on the final measurement error are clarified. The magnetometer has been tested and calibrated according to the ellipsoid fitting principle of the least squares method. To analyze the characteristics of measurement error factors, we have designed a compensation algorithm that does not depend on environmental information of the influence of external or system interference, which reduces the absolute measurement error of the magnetometer by 61.49% and effectively compensates for external or system interference. The continuous-discrete Kalman filter algorithm of the \"current\" model is established to reduce the measurement error such that the absolute measurement error of the magnetometer is further reduced by 42.36% relative to the compensated data, which effectively eliminates the error caused by jumping points and noise.  
The experimental results show that the combined attitude angle measurement scheme of the magnetometer-satellite positioning system can improve measurement accuracy by effectively compensating for interference and by reducing jumping points or noise error. This proves the feasibility and effectiveness of the scheme, its suitability for UAV attitude angle measurement, and its high engineering application value.  
Literature review and analysis, G.Q. and Z.Z.; writing G.Q. and J.L.; experiment--analysis, Z.Z., Y.D. and Z.S.; experiment--performance G.Q. and A.G. All authors have read and agreed to the published version of the manuscript.  
Shaanxi Province Key R&D Program(2021 ZDLGY09-08), Shaanxi Province Natural Science Basic Research Program(2022 JQ-060), Defense Fund (2021-JCQ-JJ-0805 and 2021-JCQ-JJ-0790), Natural Science Youth Fund project of Shaanxi Province2021 JQ-858, General Aviation Technology Center Fund ((AHY-2016084), Shaanxi Province Science and Technology Key Laboratory Fund (6142101200108), Shaanxi Higher Education Teaching Reform Research Project (21 BY163), Youth Science Fund(62103441), Key Projects of Shaanxi Province2022 JZ-37, Scientific Research Foundation of Xi'an Aeronautical Institute (2021 KY0210 and 2021 KY0209), Special scientific Research Project of Education Department of Shaanxi Province21 JK0701.  
Figure 12: Data error before and after filtering.  
**Institutional Review Board Statement:** The article does not cover human research, so it does not cover this item.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge financial support from Shaanxi Province Key R&D Program(2021 ZDLGY09-08), Shaanxi Province Natural Science Basic Research Program(2022)Q-060), Defense Fund (2021-JCJQ-JJ-0805 and 2021-JCJQ-JJ-0790), Natural Science Youth Fund project of Shaanxi Province2021 JQ-858, General Aviation Technology Center Fund (XHY-2016084), National Defense Science and Technology Key Laboratory Fund (6142101200108), Shaanxi Higher Education Teaching Reform Research Project (21 BY163), Youth Science Fund(62103441), Key Projects of Shaanxi Province2022 JZ-37,Scientific Research Foundation of Xi'an Aeronautical Institute (2021 KY0210 and 2021 KY0209), Special scientific Research Project of Education Department of Shaanxi Province21 JK0701.  
**Conflicts of Interest:** The authors declare no conflict of interest.