Wifi Fingerprint Location Algorithm in Indoor Location Based on Signal Mean Filter+k-means+WKNN

WANG Lun-Jie; LIAO Xing-Yu; PAN Wei-Jie; LV Jian

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Wifi Fingerprint Location Algorithm in Indoor Location Based on Signal Mean Filter+k-means+WKNN

更新时间：2026-09-04

- Wifi Fingerprint Location Algorithm in Indoor Location Based on Signal Mean Filter+k-means+WKNN
- Vol. 34, Issue 3, Pages: 30-34(2017)
- 作者机构：
  
  1. 贵州大学现代制造技术教育部重点实验室机构,贵州,贵阳,550000
  2. 中南大学信息科学与工程学院,湖南,长沙,410006
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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汪伦杰, 廖兴宇, 潘伟杰, 吕健. 基于信号均值滤波+k-means+WKNN的Wifi指纹定位算法研究[J]. 微电子学与计算机, 2017, 34(3): 30-34.

WANG Lun-Jie, LIAO Xing-Yu, PAN Wei-Jie, et al. Wifi Fingerprint Location Algorithm in Indoor Location Based on Signal Mean Filter+k-means+WKNN[J]. 2017, 34(3): 30-34.
汪伦杰, 廖兴宇, 潘伟杰, 吕健. 基于信号均值滤波+k-means+WKNN的Wifi指纹定位算法研究[J]. 微电子学与计算机, 2017, 34(3): 30-34. DOI：

WANG Lun-Jie, LIAO Xing-Yu, PAN Wei-Jie, et al. Wifi Fingerprint Location Algorithm in Indoor Location Based on Signal Mean Filter+k-means+WKNN[J]. 2017, 34(3): 30-34. DOI：

摘要

首先利用平滑均值滤波方法对采集信号的RSSI值进行均值平滑处理

提高信号采集的精度; 其次利用k-means聚类算法对指纹库进行优化提高指纹库的精度; 最后在KNN算法的基础上采用加权的方法来进行改进

形成新的指纹算法WKNN

并将新的指纹匹配算法WKNN应用到文本设计的Wifi指纹定位系统中.实验结果表明: 该改进算法在定位精度和稳定性方面较传统Wifi指纹定位算法有大幅度的提高.

Abstract

In this paper

we first make use of the smoothing mean filtering method to smooth the RSSI value of the signal in order to improve the accuracy of signal acquisition.Secondly

we use k-means clustering algorithm to optimize the fingerprint database in order to improve the accuracy of the fingerprint database.Finally

we used a weighted approach to improve the traditional KNN algorithm and form a new fingerprint matching algorithm WKKN.The experimental results show that: Improved algorithm proposed in positioning accuracy and stability than traditional fingerprint Wifi positioning considered to have greatly improved.

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references

Fang S H, Liu T N, Lee K C. A novel algorithm for multipath fingerprinting in Indoor WLAN environments[J]. IEEE Transactions on Wireless Communications, 2008, 7(9): 3579-3588. DOI: 3579

Alonso J M, Ocana M, Hernandez N, et al. Enhanced WiFi localization system based on soft computing techniques to deal with small-scale variations in wireless sensors[J]. Applied Soft Computing, 2011, 11(8): 4677-4691. DOI: 4677

Huaiyu Li, Xiuwan Chen, Guifei Jing, et al. An indoor continuous positioning algorithm on the move by fusing sensors and WiFi on smart phones[J]. Sensor, 2015, 15(12): 31244-31267. DOI: 31244

Wang J, Hu A, Liu C, et al. A floor-map-aided WiFi/pseudo-odometry integration algorithm for an indoor positioning system[J]. Sensors, 2015, 15(4): 7096-7124. DOI: 7096

Christos Laoudias, Robert Piche, Christos G. Device self-calibration in location systems using signal strength histograms[J]. Journal of Location Based Services, 2013, 7(3): 165-181. DOI: 165

Yang Z, Zhou Z, Liu Y. From RSSI to CSI: Indoor localization via channel response[J]. ACM Computing Surveys, 2013, 46(2): 1-25. DOI: 1

Galvan Tejada I, Sandoval E I, Brena R. Wifi bluetooth based combined positioning algorithm[J]. proceed Engineering, 2012, 7(4): 101-108. DOI: 101

Zandbergen P A. Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning[J]. Transaction in GIS, 2009, 13(S1): 5-25. DOI: 5

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