Abstract
An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.
Highlights
Indoor positioning is used in areas where the global positioning system (GPS) is not desirable
In this paper, proposed algorithm Q-weighted k-nearest neighbor (WKNN) is the improvement of WKNN
WKNN, M-WKNN, and the common positioning algorithms Gaussian kernel (GK) and least squares-support vector machine (LS-support vector machine (SVM)) were compared with Q-WKNN to verify its positioning accuracy and real-time performance
Summary
Indoor positioning is used in areas where the global positioning system (GPS) is not desirable. The extensive deployment of wireless infrastructure and the proliferation of mobile devices have facilitated positioning in indoor scenes, and positioning based on received signal strength (RSS) has been an attractive solution [1]. Common wireless signals such as Bluetooth [2], Wi-Fi [3], ultrawideband (UWB) [4], and radio frequency identification (RFID) [5] are often used for positioning. In terms of measurement techniques, the common methods include time of arrival (TOA), angle of arrival (AOA), time difference of arrival (TDOA), and received signal strength (RSS) [6]. Wi-Fi—based indoor positioning has gone viral for advantages such as no need for additional hardware assistance except access points (APs) [7], adaption to various indoor environments, and convenient acquisition of RSS
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