An RSS Transform-Based WKNN for Indoor Positioning

被引：24

作者：

Zhou, Rong ^{[1
]}

Yang, Yexi ^{[1
]}

Chen, Puchun ^{[1
]}

机构：

[1] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China

来源：

SENSORS | 2021年 / 21卷 / 17期

关键词：

Wi-Fi fingerprint; RSS fluctuation; AP selection; WKNN; ACCESS-POINT SELECTION; LOCATION; LOCALIZATION;

D O I：

10.3390/s21175685

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：18

共 38 条

[1] DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier [J].

Ashraf, Imran ;

Hur, Soojung ;

Park, Sangjoon ;

Park, Yongwan .

SENSORS, 2020, 20 (01)

[2] Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area [J].

Ashraf, Imran ;

Hur, Soojung ;

Park, Yongwan .

SENSORS, 2019, 19 (19)

[3]

Bahl P., 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), P775, DOI 10.1109/INFCOM.2000.832252

[4] Transfer Learning for Wireless Fingerprinting Localization Based on Optimal Transport [J].

Bai, Siqi ;

Luo, Yongjie ;

Wan, Qun .

SENSORS, 2020, 20 (23) :1-21

[5]

Bozkurt S, 2015, 2015 INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA) PROCEEDINGS, P47

[6] Power-efficient access-point selection for indoor location estimation [J].

Chen, Yiqiang ;

Yang, Qiang ;

Yin, Jie ;

Chai, Xiaoyong .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2006, 18 (07) :877-888

[7]

ChenshuWu Jingao Xu, 2017, PROC ACM INTERACTIVE, V1, P29

[8]

Dempster A.G., P INT GLOB NAV SAT S

[9]

Deng ZA, 2011, 2011 6TH INTERNATIONAL ICST CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA (CHINACOM), P257, DOI 10.1109/ChinaCom.2011.6158159

[10] A Map-Assisted WiFi AP Placement Algorithm Enabling Mobile Device's Indoor Positioning [J].

Du, Xuan ;

Yang, Kun .

IEEE SYSTEMS JOURNAL, 2017, 11 (03) :1467-1475

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