Improvement the Accuracy of Attitude Estimation of UAV using the Extended Kalman Filter based on Particle Swarm Optimization

Ammar Assad, Sergey Serikov

Abstract


The issue of Unmanned Aerial Vehicle (UAV) attitude estimation has been extensively studied, yet researchers continue to seek improvements, recognizing that more precise attitude calculations and determination lead to enhance UAV control robustness and accuracy. This research develops and implements an innovative approach combining Particle Swarm Optimization (PSO) with the Extended Kalman Filter (EKF) to improve UAV attitude estimation. The methodology employs raw measurements from gyroscopes, accelerometers, and magnetometers. PSO is utilized to estimate the noise covariance matrix of these measurements, which is then integrated into the EKF process to achieve superior attitude estimation results. Given that PSO is a global optimization tool, its integration with EKF demonstrated superior performance compared to the standard EKF. The implementation was carried out in a Matlab environment, using quaternions to represent UAV attitude. The mean and standard deviation (STD) of estimation errors were calculated, revealing that the PSO-EKF approach significantly enhances estimation accuracy compared to using EKF alone. Comparison with state of art results using Root Mean Square Error of attitude angles, showed that the developed method outperformed existed researches in the field of attitude estimation

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