Performance analysis of fingerprinting indoor positioning methods with BLE

作者：

Highlights：

• Wk-NN algorithm shows the best overall performance.

• SVM require less training time for optimal accuracy.

• Low performance of probabilistic-based algorithms.

• Wk-NN is the preferable option for large mixed environments.

• SVM is the preferable option for small indoor areas.

摘要

•Wk-NN algorithm shows the best overall performance.•SVM require less training time for optimal accuracy.•Low performance of probabilistic-based algorithms.•Wk-NN is the preferable option for large mixed environments.•SVM is the preferable option for small indoor areas.

论文关键词：Fingerprinting,BLE,Indoor positioning,Support Vector Machines (SVM),Multilayer Perceptron (MLP),Weighted k-Nearest Neighbours (Wk-NN)

论文评审过程：Received 29 July 2021, Revised 18 November 2021, Accepted 28 March 2022, Available online 21 April 2022, Version of Record 5 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117095