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An Improved WKNN Algorithm Based on Flexible K Selection Strategy and Distance Compensation for Indoor Localization

  • Research Article-Electrical Engineering
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Abstract

Weighted K-nearest neighbor algorithm (WKNN) is the most commonly used algorithm based on Wi-Fi. However, the accuracy of traditional algorithm is not very high due to the inflexible strategy and the ignorance of distance corresponding relationship. In this paper, an improved WKNN algorithm using fingerprint method for indoor positioning called DC-WKNN is proposed, which based on flexible K selection strategy and distance compensation. The fingerprint database is filled by fuzzy C-Means (FCM) in the offline training stage, and the online positioning stage consists of two positioning phases named coarse positioning and fine positioning. An improved K selection strategy based on relative density is firstly proposed to choose the value of K flexibly according to different test point in coarse positioning phase. Then, an WKNN algorithm based on distance compensation (DC) for adjusting the characteristic distance and realizing fine positioning is proposed. The experimental results show that the average positioning error of proposed algorithm is less than 1.1m, which reduce by 11.1%, 10% and 6.5%.

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Author notes

  1. J. Luo, J. Xiao: These authors contributed equally to this work.

Authors and Affiliations

  1. School of Physics and Electronics, Central South University, Xiaoxiang Middle Road, Changsha, 410083, Hunan, China

    Jiuqi Luo, Jiaxun Xiao & Changgeng Li

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  1. Jiuqi Luo
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  2. Jiaxun Xiao
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  3. Changgeng Li
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Correspondence to Changgeng Li.

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Luo, J., Xiao, J. & Li, C. An Improved WKNN Algorithm Based on Flexible K Selection Strategy and Distance Compensation for Indoor Localization. Arab J Sci Eng 47, 13917–13925 (2022). https://doi.org/10.1007/s13369-022-06596-w

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  • DOI: https://doi.org/10.1007/s13369-022-06596-w

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