On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy - Gimo C. Fernando; Tinghao Qi; Edmund V. Ndimbo; Assefa Tesfay Abraha; Bang Wang (Sensors)
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.