The proposed QMD algorithm effectively detects hard magnetic disturbances encountered during walking experiments using a consumer-grade IMU. Experiments were conducted in both magnetic disturbance fields and real indoor environments, with results showing improved accuracy over classical QMD methods. The IMU logs data at 100 Hz for real-time visualization and analysis. Key findings indicate that while classical QMD struggles with hard magnetic fields, the proposed algorithm successfully maintains accurate heading detection, proving its effectiveness in diverse conditions and contributing to future navigational systems.
The experiments conducted using the Mtw Awinda IMU evaluated its performance under magnetic disturbances and regular indoor environments, demonstrating effective logging and visualization capabilities.
The proposed QMD algorithm outperforms the classical method by correctly identifying and handling hard magnetic disturbances, which traditionally lead to incorrect readings in magnetic heading.
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