Abstract
This paper presents a fingerprint indoor localization system based on Bisecting k-means (BKM). Compared to k-means, BKM is a more robust clustering algorithm. Specifically, BKM based indoor localization consists of two stages: offline stage and online positioning stage. In the offline stage, BKM is used to divide all the reference points (RPs) into k clusters. A series of experiments have been made to show that our system can greatly improve localization accuracy.
The financial support of the program of Key Industry Innovation Chain of Shaanxi Province, China (2017ZDCXL-GY-04-02), of the program of Xi’an Science and Technology Plan (201805029YD7CG13(5)), Shaanxi, China, of National S&T Major Project (No. 2016ZX03001022-003), China, and of Key R&D Program - The Industry Project of Shaanxi (Grant No. 2018GY-017) are gratefully acknowledged.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, Y., Liu, W., Zhao, H., Cao, S., Fu, S., Jiang, D. (2019). Bisecting K-Means Based Fingerprint Indoor Localization. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-32216-8_1
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DOI: https://doi.org/10.1007/978-3-030-32216-8_1
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