Improving Attitude and Heading Reference Systems Performance Via Machine Learning-Driven Parameters Fine-Tuning

Attitude and Heading Reference Systems (AHRSs) based on Error-State Extended Kalman Filters (ES-EKF) require careful tuning of stochastic parameters to achieve optimal performance. Traditional approaches rely on Allan Variance (AV) analysis for parameter identification, which, while physically grounded, does not guarantee optimal navi- gation performance. This paper presents three advanced opti- mization methodologies—Gaussian Processes (GP), Nondomi- nated Sorting Genetic Algorithm III (NSGA-III), and Multi- Objective Tree-structured Parzen Estimator (MO-TPE)—to systematically fine-tune critical ES-EKF parameters directly against flight performance metrics. The optimization targets accelerometer/magnetometer noise/bias characteristics, vehicle dynamics/Earth magnetic field uncertainty, and Innovation Fil- ter (IF) thresholds across a multi-dimensional parameter space. Experimental validation using 19 real Unmanned Aerial Vehi- cle (UAV) flights demonstrates substantial improvements over traditional AV-based tuning. The proposed methods achieve 81- 91% reductions in Root Mean Square (RMS) attitude errors, 82-91% improvements in Mean Absolute Errors (MAE), and 41-93% enhancements in estimation consistency across roll, pitch, and yaw axes. MO-TPE consistently delivers the best overall performance, achieving optimal results in 7 out of 9 metric-axis combinations, followed closely by NSGA-III, while GP provides competitive single-objective optimization. The largest improvements are observed in yaw estimation, where traditional approaches struggle most with magnetic disturbances. These results demonstrate that machine learning- driven parameter optimization can significantly enhance AHRS accuracy and robustness without modifying the underlying ES-EKF structure, offering a practical path for improving navigation performance in real-world UAV applications.