Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method
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Enhancing DF-INS for Accurate Zero-Velocity Detection in ILBS: A Dual Foot Synergistic Method
Renjie Wu,
Boon Giin Lee,
Matthew Pike,
Linzhen Zhu,
Xiaoqing Chai,
Yongfu Wang
IEEE Sensors Journal | 2023
| View on Publisher's Website
Abstract
The Dual Foot Inertial Navigation System (DF-INS) is a promising approach for indoor location-based services (ILBS). Achieving accurate zero-velocity detection is crucial for optimal performance in zero-velocity updating and trajectory calculation. However, conventional techniques rely on fixed thresholds, which are unsuitable for dynamic scenarios and diverse users. This study introduces a dual foot synergistic method to improve zero-velocity detection accuracy. The proposed method smooths the General Likelihood Ratio Test (GLRT) sequences from both feet using a moving average filter and identifies points of equality as transition markers between stance and swing phases. Experimental results on a closed indoor path show that the proposed method outperforms conventional fixed-threshold techniques in zero-velocity detection and DF-INS accuracy. This work contributes to the development of more robust ILBS solutions, particularly for wearable navigation systems.