Abstract
Utilizing Wi-Fi signals for indoor localization significantly improves location-based services in indoor environments, though challenges arise due to unpredictable Wi-Fi signal propagation. We propose an innovative Feature Enhancement and K-Nearest Neighbor (FEKNN) approach, which refines RSSI data distribution for a more accurate feature database and employs a refined Weighted K-Nearest Neighbor (W-KNN) algorithm to calculate locations by Euclidean distances between enhanced features. Extensive experiments validate that our FEKNN has remarkable accuracy for indoor localization applications, achieving state-of-the-art performance with an impressive average localization error of 1.86 meters on the public UjiIndoorLoc testing dataset, and an average error of 0.68 meters on our custom-built dataset.
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Acknowledgements
This work is supported in part by the Beijing Natural Science Foundation L231013, and in part by the National Science Foundation of China (62376271, U21A20515, U22B2034, 62365014, 62262043, and 62171321).
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Wang, J., Yang, J., Li, B., Meng, W., Zhang, J., Zhang, X. (2025). FEKNN: A Wi-Fi Indoor Localization Method Based on Feature Enhancement and KNN. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14997. Springer, Cham. https://doi.org/10.1007/978-3-031-71464-1_1
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