Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments.

Wen Liu,Zhongliang Deng,Xiao Fu

doi:10.3390/s16122055

Wen Liu, Zhongliang Deng + Show 1 more

Open Access

https://doi.org/10.3390/s16122055

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Abstract

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

Highlights

Indoor location-based service (ILBS) has gained considerable attention in recent years due to its social and commercial values, with market value predicted to worth US $10 billion by 2020 [1].the demand for accurate localization in indoor environments, such as large public places, office buildings with mass obstacles and military facilities, has increased dramatically [2,3]
Since the deployment of access points (AP) in Wi-Fi-covered indoor environments has been fixed, and traditional clustering algorithms are not robust enough for indoor positioning, this paper proposed a novel clustering method based on the coordinates of Reference Points (RPs) together with their Received Signal Strength (RSS)
The distributions of the RPs and their RSS values in experimental fingerprint database are investigated, and the distribution results both in scatter diagram and contour diagram are presented in Figure 6 and Figure 7

Summary

Introduction

Indoor location-based service (ILBS) has gained considerable attention in recent years due to its social and commercial values, with market value predicted to worth US $10 billion by 2020 [1].the demand for accurate localization in indoor environments, such as large public places, office buildings with mass obstacles and military facilities, has increased dramatically [2,3]. Local Area Network (WLAN), Bluetooth, ZigBee, Radio Frequency Identification Devices (RFID), Pseudolite and Ultra Wideband (UWB), have been proposed to provide better performance in indoor localization. Infrared and Ultrasonic positioning technology could achieve centimeter-level accuracy in Line-Of-Sight environments [4,5], these two technologies both need the implementation of dense access points (AP) and the obstacles in indoor environments would have a considerable influence on the accuracy and robustness of the system. Pseudolite and UWB positioning technology can perform a centimeter-level accuracy at the cost of expensive special devices and high system. Among WLAN, Bluetooth, ZigBee and RFID positioning technologies, WLAN has aroused researchers’ interests because of its wide implementation, high mobility, low networking cost and high compatibility in dense cluttered indoor environments [8,9,10,11]

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