Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (7): 2444-2454.doi: 10.13229/j.cnki.jdxbgxb.20231101

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CSI Passive indoor fingerprint positioning method based on improved weighted K⁃nearest neighbor algorithm

Xiao-qiang SHAO1,2(),Bo MA1,2,Ze-hui HAN1,2,Yong-de YANG1,2,Ze-wen YUAN1,2,Xin LI1,2   

  1. 1.College of Electrical and Control Engineering，Xi'an University of Science and Technology，Xi'an 710054，China
    2.Xi'an Key Laboratory of Electrical Equipment Condition Monitoring and Power Supply Security，Xi'an University of Scienec and Technology，Xi'an 710054，China
  • Received:2023-10-15 Online:2025-07-01 Published:2025-09-12

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Abstract:

A passive indoor positioning method based on improved weighted K-nearest neighbor algorithm is proposed to address the problem of low positioning accuracy caused by excessive interference in amplitude and phase construction. In the offline stage， the isolation forest method is adopted， and the wavelet domain denoising and linear transformation method with improved threshold are used to preprocess the collected channel state information. The processed amplitude and phase information is used together as fingerprint data to construct a stable fingerprint database related to the reference point position information. In the online stage， an improved weighted K-nearest neighbor algorithm is proposed to repeatedly match the estimated coordinates. After obtaining the position coordinates in a single match， the algorithm calculates the Euclidean distance of the position coordinates between K-nearest neighbor points， and uses Gaussian transformation to calculate the weight of the K distance values， completing personnel positioning. Experimental simulation tests were conducted in classrooms and halls， and it was found that approximately 81% of the proposed algorithm's testing position error was controlled within 1 meter， which can effectively improve positioning accuracy.

Key words: indoor positioning, channel state information, passive positioning, improved threshold denoising in wavelet domain, improved weighted K-nearest neighbor algorithm, Gaussian transformation

CLC Number: 

  • TN92

Fig.1

Isolated forest before and after treatment"

Fig.2

Change of threshold function with m"

Fig.3

Amplitude before and after denoising"

Fig.4

Phase transformation process diagram through linear transformation"

Fig.5

Fingerprint positioning architecture diagram"

Fig.6

Classroom 501（schematic diagram of experimental environment）"

Fig.7

Coal Science and technology building hall （experimental environment diagram）"

Fig.8

Impact of number of data packets on positioning accuracy"

Fig.9

Classroom 501 （unmanned）CDF for data quality"

Fig.10

Building hall CDF for data quality"

Fig.11

Average error of different Kvalues for two different algorithms"

Fig.12

Comparison of error between two algorithms"

Fig.13

Comparison chart of error of the algorithm in this article under different interferences"

Fig.14

Cumulative distribution function diagram of six positioning algorithm errors"

[1] 陈锐志, 郭光毅, 陈亮, 等.室内高精度定位技术研究应用现状与发展趋势[J]. 武汉大学学报: 信息科学版, 2023, 48(10): 1591-1600.
Chen Rui-zhi, Guo Guang-yi, Chen Liang, et al. R-esearch and application status and development tre-nds of indoor high precision positioning technology[J]. Journal of Wuhan University(Information ScienceEdition), 2023,48(10):1591-1600.
[2] 张楠, 史建华, 亿吉, 等. 基于加权质心定位的井下移动目标实时跟踪方法[J]. 吉林大学学报: 工学版, 2023, 53(5): 1458-1464.
Zhang Nan, Shi Jian-hua, Yi Ji,et al. Real-time trac-king method of underground moving target based on weighted centroid location[J]. Journal of Jilin University (Engineering and Technology Edition), 2023,53(5):1458-1464.
[3] Liu J, Wang L, Guo L, et al. A research on CSI ba-sed human motion detection in complex scenarios[C]∥Proceedings of the IEEE 19th International Conference on E-Health Networking, Applications and Services, Dalian, China,2017:1-6.
[4] 王玫, 宋志远. 基于TrAdaBoost算法为内核的行人航迹推算技术[J]. 吉林大学学报: 工学版, 2023, 53(8): 2364-2370.
Wang Mei, Song Zhi-yuan. Pedestrian dead reckoningtechnology based on TrAdaBoost algorithm[J]. Journalof Jilin University (Engineering and Technology Edition), 2023, 53(8): 2364-2370.
[5] 张海军, 孙学成, 赵小虎,等. 煤矿井下UWB人员定位系统研究[J]. 工矿自动化, 2022, 48(2): 29-34.
Zhang Hai-jun, Sun Xue-cheng, Zhao Xiao-hu, et al.Research on UWB personnel positioning system in coal mine[J]. Industrial and Mining Automation,2022, 48(2): 29-34.
[6] Wei F, Wu Y, Xu S W, et al. Accurate visible lightpositioning technique using extreme learning machineand meta-heuristic algorithm[J]. Optics Communications, 2023, 532:No. 129245.
[7] 郭苏, 邱继芸, 余江涛, 等. 变电站室内导航系统蓝牙定位技术与算法研究[J]. 信息技术, 2020, 44(8): 141-145.
Guo Su, Qiu Ji-yun, Yu Jiang-tao, et al. Research on Bluetooth localization technology and algorithm of substa-tion indoor navigation system[J]. Information technology, 2020, 44(8): 141-145.
[8] Wang F, Feng J, Zhao Y, et al. Joint activity recognition and indoor localization with WiFi fingerprints[J]. IEEE Access 2019,7:80058-80068.
[9] Zhuo Y, Zhu H, Xue H, et al. Perceiving accurate CSI phases with commodity Wi-Fi devices[C]∥Proceedings of the IEEE INFOCOM—IEEE Conference on Computer Communications, Atlanta, USA, 2017: 1-9.
[10] Yang W, Gong L, Man D, et al. Enhancing the perf-ormance of indoor device-free passive localization[J]. International Journal of Distributed Sensor Networ-ks, 2015, 11(11): 79-90.
[11] Wang X Y, Gao L J, Mao S W, et al. DeepFi:deep lear-ning for indoor fingerprinting using channel stateinforma-tion[C]∥IEEE Wireless Communications and Net-working Conference, New Orleans, USA, 2015: 1666-1671.
[12] Rao X P, Li Z, Yang Y B, et al. Device-free passivewireless localization system with weighted transferable discriminative dimensionality reduction method[J]. Teleommunication Systems: Modeling, Analysis, Designand Management,2020,75(1):15-29.
[13] 王博远, 贾瑞才, 贾浩男. 高精度位置服务终端技术与应用[J].无线电工程, 2016, 46(11): 5-8.
Wang Bo-yuan, Jia Rui-cai, Jia Hao-nan. High preci-sion location service terminal technology and applica-tion[J]. Radio Engineering, 2016, 46(11): 5-8.
[14] Wu C S, Zheng Y, Zhou Z M, et al.Phase U:real-time LOS Identification with Wi-Fi[C]∥Computer Communications, Hong Kong,China, 2015:2038-2046.
[15] Fang N, Fang X W, Lu K. Anomalous Behavior detec-tion based on the isolation forest model with multi-ple perspective business processes[J]. Electronics, 2022, 11(21): No. 3640.
[16] Wu Y, Gao G, Cui C. Improved wavelet denoising by non-convex sparse regularization under double wavelet domains[J]. IEEE Access 2019, 7: 30659-30671.
[17] Zhuo Y, Zhu H, Xue H. Identifying a new non-linear CSI phase measurement error with commodity WiFi devices[C]∥Proceedings of the IEEE 22nd International Conference on Parallel and Distributed Systems(ICPADS), Wuhan, China, 2016:72-79.
[18] 邓中亮, 尹露, 唐诗浩.室内定位关键技术综述[J].导航定位与授时, 2018, 5(3): 14-23.
Deng Zhong-liang, Yin Lu, Tang Shi-hao. Overviewof key techniques of indoor positioning[J]. Navigation Positioning and Timing, 2018, 5(3): 14-23.
[19] 史林军, 戴滔, 劳文洁, 等. 基于改进KNN算法的新能源发电单元运行状态识别[J]. 电力自动化设备, 2024(5): 65-72.
Shi Lin-jun, Dai Tao, Lao Wen-jie, et al. New energypower generation unit operating state recognition bas-ed on improved KNN algorithm[J]. Power automation equipment, 2024(5): 65-72.
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