Enhancing UAV Navigation Accuracy Through EKF Magnetic Interference Estimation Algorithm

This thesis addresses a critical challenge in UAV navigation by investigating and mitigating the impact of magnetic interference on Inertial Navigation Systems (INS). It emphasizes the importance of accurate attitude determination for UAV flight stability and identifies the susceptibility of magnetometers to interference in complex electrical environments. A comprehensive model is developed to describe the relationship between actual magnetic measurements and the output of geomagnetic sensors, considering soft and hard magnetic interference factors. Comparative experiments reveal significant interference sources, leading to the proposal of a novel magnetic interference estimation algorithm and a swift compass calibration method tailored for UAVs. Integrating theoretical modeling with practical experimentation, the study advances understanding and solutions for enhancing UAV navigation accuracy and reliability. The proposed methodology contributes valuable insights and practical solutions, laying a foundation for further research in the field. Furthermore, the study introduces a high-precision magnetic interference estimation algorithm based on Extended Kalman Filtering (EKF), providing users with real-time feedback and facilitating quick compass calibration. This research contributes to the improvement of overall flight performance and safety in UAV navigation systems, particularly in the presence of magnetic interference.