Fernando Gimo C, Qi Tinghao, Ndimbo Edmund V, Abraha Assefa Tesfay, Wang Bang
School of Information, Electronic and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.
Mekelle Institute of Technology, Mekelle University, Mekelle 7000, Ethiopia.
Sensors (Basel). 2025 Feb 20;25(5):1294. doi: 10.3390/s25051294.
Accurate indoor positioning remains a critical challenge due to the limitations of single-source systems, such as signal instability and environmental obstructions. This study introduces a multi-source fusion positioning algorithm that integrates inertial sensors and signal fingerprints to address these issues. Using a weighted fusion method, the algorithm employs pedestrian dead reckoning (PDR) for trajectory tracking and combines its outputs with wireless signal fingerprints. Experimental evaluations conducted on diverse trajectories reveal significant improvements in accuracy, achieving a 35.3% enhancement over wireless-only systems and a 71.4% improvement compared to standalone PDR. The proposed method effectively balances computational efficiency and accuracy, demonstrating robustness in complex and dynamic indoor environments. These findings establish the algorithm's potential for practical applications in navigation, robotics, and Industry 4.0, where precise indoor localization is essential.
由于单源系统存在信号不稳定和环境障碍物等局限性，精确的室内定位仍然是一项严峻的挑战。本研究引入了一种多源融合定位算法，该算法集成了惯性传感器和信号指纹来解决这些问题。该算法采用加权融合方法，利用行人航位推算（PDR）进行轨迹跟踪，并将其输出与无线信号指纹相结合。在不同轨迹上进行的实验评估显示，定位精度有显著提高，与仅使用无线系统相比提高了35.3%，与独立的PDR相比提高了71.4%。所提出的方法有效地平衡了计算效率和准确性，在复杂和动态的室内环境中表现出鲁棒性。这些发现确立了该算法在导航、机器人技术和工业4.0等实际应用中的潜力，在这些领域中精确的室内定位至关重要。
