Abstract

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

Highlights

  • The indoor positioning technology is a research focus on navigation and location-based services (LBS), and has attracted extensive attentions of research institutions, universities and enterprises.Nowadays, there are a large number of applications for ILBS in market

  • This study proposes a localization approach based on the omnidirectional fingerprint database (OFPD) and twice affinity propagation clustering (APC) algorithm

  • This paper focuses on Wi-Fi fingerprinting method for indoor introduced for Wi‐Fi fingerprinting localization in [7] in detail

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Summary

Introduction

The indoor positioning technology is a research focus on navigation and location-based services (LBS), and has attracted extensive attentions of research institutions, universities and enterprises. There are a large number of applications for ILBS in market. Researchers have carried out a large number of studies on indoor positioning technologies and developed corresponding indoor positioning systems, such as the infrared [1], ultrasonic [2] or sound [3], radio frequency identity [4,5] (RFID), ZigBee [6], Wi-Fi [7,8], Bluetooth [9], microelectromechanical systems (MEMS). The Wi-Fi fingerprinting technology is the most feasible and cost effective technique for indoor localization, without knowing locations of APs in advance. The fingerprinting localization contains two stages, offline stage and online stage. The basic idea of fingerprinting localization for estimating user’s position is matching the online received signal strength (RSS) readings with offline prebuilt fingerprint database. The fingerprint database, named as Sensors 2018, 18, 2502; doi:10.3390/s18082502 www.mdpi.com/journal/sensors

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