Smallest enclosing circle-based fingerprint clustering and modified-WKNN matching algorithm for indoor positioning

Liu, Wenjun; Fu, Xiao; Deng, Zhongliang; Xu, Lianming; Jiao, Jichao

doi:10.1109/ipin.2016.7743694

Cited by 33 publications

(22 citation statements)

References 10 publications

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“…Liu et al [36] proposed a strategy using the smallest enclosing circle algorithm (SEC) which is widely used in the field of information security. They directly input the RP's coordinates instead of the RSS, and the smallest enclosing circle is the smallest circle that consists of all points to be positioned.…”

Section: Empirical Modelingmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: Empirical Modelingmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

195

125

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“…Recently works on smartphone-based with Wi-Fi fingerprinting have reached a mean distance error of around 5m [1,6]. For learning techniques, the well-known K-Nearest Neighbors (KNN) and its alternative are among the most popular technique [7,8]. In [9], the authors differ a wide range of KNN parameters to get a set of models.…”

Section: Related Workmentioning

confidence: 99%

Smartphone-Based User Positioning in a Multiple-User Context with Wi-Fi and Bluetooth

Dao

Vaufreydaz

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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In a multi-user context, the Bluetooth data from the smartphone could give an approximation of the distance between users. Meanwhile, the Wi-Fi data can be used to calculate the user's position directly. However, both the Wi-Fi-based position outputs and Bluetooth-based distances are affected by some degree of noise. In our work, we propose several approaches to combine the two types of outputs for improving the tracking accuracy in the context of collaborative positioning. The two proposed approaches attempt to build a model for measuring the errors of the Bluetooth output and Wi-Fi output. In a nontemporal approach, the model establishes the relationship in a specific interval of the Bluetooth output and Wi-Fi output. In a temporal approach, the error measurement model is expanded to include the time component between users' movement. To evaluate the performance of the two approaches, we collected the data from several multi-user scenarios in indoor environment. The results show that the proposed approaches could reach a distance error around 3.0m for 75 percent of time, which outperforms the positioning results of the standard Wi-Fi fingerprinting model.

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“…All of these test points were selected randomly in the experimental scenarios, and the systems’ positioning errors were measured for analysis. Regarding to the matching algorithm of fingerprint positioning system, this paper firstly conducted three matching algorithms, namely K Nearest Neighbor (KNN), Weight K Nearest Neighbor (WKNN) and Modified Weight K Nearest Neighbor (MWKNN) [23], in Test-bed 2 Floor 1 to evaluate the performance of positioning system. The comparison of positioning errors in cumulative distribution function (CDF) for these three matching algorithms has been shown in Figure 11.…”

Section: Measurement Analysismentioning

confidence: 99%

“…We had previously proposed a smallest-enclosing-circle-based clustering algorithm for indoor positioning in [23]. This paper is an extension of the work in [23], and, in this paper, the AP implementation influence is detailed analyzed.…”

Section: Introductionmentioning

confidence: 96%

“…This paper is an extension of the work in [23], and, in this paper, the AP implementation influence is detailed analyzed. In particular, when compared to [23], in this paper, the smallest-enclosing-circle-based clustering algorithm is described and derived in detail, and new experiments are conducted in different environments to demonstrate the effectiveness of the method proposed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Deng

2016

Sensors

Self Cite

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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.

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Smallest enclosing circle-based fingerprint clustering and modified-WKNN matching algorithm for indoor positioning

Cited by 33 publications

References 10 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Smartphone-Based User Positioning in a Multiple-User Context with Wi-Fi and Bluetooth

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

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