AMO-HEAD: Adaptive MARG-Only Heading Estimation for UAVs under Magnetic Disturbances

2025-10-12

AI Summary

Indoor magnetic disturbances severely degrade heading estimation accuracy for unmanned aerial vehicles (UAVs). Method: This paper proposes a lightweight, adaptive MARG-only heading estimation algorithm. It employs an improved quaternion-based extended Kalman filter (EKF) that fuses accelerometer and magnetometer measurements; incorporates an adaptive process noise covariance mechanism to suppress gyroscope drift; and introduces a real-time magnetic disturbance detection module that dynamically adjusts a scaling factor to compensate for external magnetic interference. A theoretical observability analysis of the system is also conducted. Contribution/Results: Experimental evaluation in realistic, strongly magnetically disturbed indoor environments demonstrates significant improvements in heading estimation accuracy and robustness. The algorithm maintains low computational overhead, making it suitable for resource-constrained micro-UAV platforms.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Adversarial Agents

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Accurate and robust heading estimation is crucial for unmanned aerial vehicles (UAVs) when conducting indoor inspection tasks. However, the cluttered nature of indoor environments often introduces severe magnetic disturbances, which can significantly degrade heading accuracy. To address this challenge, this paper presents an Adaptive MARG-Only Heading (AMO-HEAD) estimation approach for UAVs operating in magnetically disturbed environments. AMO-HEAD is a lightweight and computationally efficient Extended Kalman Filter (EKF) framework that leverages inertial and magnetic sensors to achieve reliable heading estimation. In the proposed approach, gyroscope angular rate measurements are integrated to propagate the quaternion state, which is subsequently corrected using accelerometer and magnetometer data. The corrected quaternion is then used to compute the UAV's heading. An adaptive process noise covariance method is introduced to model and compensate for gyroscope measurement noise, bias drift, and discretization errors arising from the Euler method integration. To mitigate the effects of external magnetic disturbances, a scaling factor is applied based on real-time magnetic deviation detection. A theoretical observability analysis of the proposed AMO-HEAD is performed using the Lie derivative. Extensive experiments were conducted in real world indoor environments with customized UAV platforms. The results demonstrate the effectiveness of the proposed algorithm in providing precise heading estimation under magnetically disturbed conditions.

Problem

• Estimating UAV heading accurately under magnetic disturbances
• Compensating for gyroscope noise and magnetic interference
• Providing lightweight heading estimation for indoor UAV inspection

Innovation

• Adaptive EKF framework for heading estimation
• Process noise covariance for gyroscope error compensation
• Real-time magnetic deviation detection with scaling factor