Open Access

ARTICLE

Attitude Estimation Using an Enhanced Error-State Kalman Filter with Multi-Sensor Fusion

Yu Tao¹, Tian Yin², Yang Jie^1,*

1 School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, 341000, China
2 CRRC Academy, Beijing, 100000, China

* Corresponding Author: Yang Jie. Email:

Journal on Artificial Intelligence 2025, 7, 549-570. https://doi.org/10.32604/jai.2025.072727

Received 02 September 2025; Accepted 21 October 2025; Issue published 01 December 2025

Abstract

To address the issue of insufficient accuracy in attitude estimation using Inertial Measurement Units (IMU), this paper proposes a multi-sensor fusion attitude estimation method based on an improved Error-State Kalman Filter (ESKF). Several adaptive mechanisms are introduced within the standard ESKF framework: first, the process noise covariance is dynamically adjusted based on gyroscope angular velocity to enhance the algorithm’s adaptability under both static and dynamic conditions; second, the Sage-Husa algorithm is employed to estimate the measurement noise covariance of the accelerometer and magnetometer in real-time, mitigating disturbances caused by external accelerations and magnetic fields. Additionally, a dual-mode correction strategy is proposed for yaw angle estimation: a computationally efficient quaternion-based direct correction method is used for small-angle errors, while the system switches to a higher-precision adaptive ESKF algorithm for large-angle deviations. This strategy ensures estimation accuracy while effectively reducing computational complexity. Experimental results in mixed static-dynamic scenarios show that the proposed algorithm achieves the lowest root mean square error (RMSE) in roll (5.638°) and yaw (6.315°), and ranks first in pitch (2.616°), validating the effectiveness of the improvements. In magnetic interference tests, it delivers the best overall performance, achieving the highest accuracy in roll and yaw and near-optimal performance in pitch, highlighting its excellent anti-interference capability and dynamic tracking performance. Complexity analysis further confirms a significant reduction in computational time compared to the standard ESKF. The results consistently demonstrate that the proposed method offers higher estimation accuracy and robustness under complex conditions, making it suitable for practical applications involving magnetic disturbances and rapid motions.

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