基于WiFi-BP的室内定位算法

doi:10.16180/j.cnki.issn1007-7820.2020.08.013

Abstract

Abstract:

Aiming at the problem that signal deviation caused by the device variability affects positioning accuracy, an indoor positioning algorithm combining BP neural network and weighted centroid positioning algorithm was proposed. In this paper, the RSSI data of different mobile phones were cleaned by outlier detection algorithm, and the cleaned data was used as the data source of BP neural network to train the model, thus obtaining a stable nonlinear BP model. On this basis, combined with the improved indoor positioning algorithm for indoor positioning. Experiment results showed the mean error, minimum error and maximum error of the proposed algorithm were 0.58 m, 0.24 m and 1.06 m, respectively, and the positioning accuracy was significantly higher than that of the existing similar algorithms.

Key words: the device variability, received signal strength indication, indoorpositioning, BP neural network, the outlier detection, weighted centroid positioning

CLC Number:

TN92

ZHU Yifeng. Indoor Positioning Algorithm Based on WiFi-BP[J].Electronic Science and Technology, 2020, 33(8): 74-79.

Figures/Tables 11

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References 16

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序号	实测坐标	加权质心定位算法	改进加权质心定位算法	本文算法
P₁	(2.69,9.32)	(1.65,11.20)	(3.12,8.07)	(3.11,8.58)
P₂	(5.14,9.14)	(5.42,10.40)	(5.69,10.52)	(5.38,10.17)
P₃	(5.18,46.75)	(6.13,6.86)	(5.83,6.96)	(5.59,6.36)
P₄	(2.92,3.76)	(2.31,4.35)	(2.38,5.65)	(2.51,4.89)
P₅	(4.90,4.64)	(5.34,4.28)	(5.18,4.24)	(5.25,4.68)
P₆	(7.39,5.96)	(7.79,5.82)	(7.08,5.72)	(7.27,5.75)

序号	加权质心算法真误差/m	改进加权质心算法真误差/m	本文算法真误差/m
P₁	1.17	0.62	0.41
P₂	0.81	0.86	0.75
P₃	0.82	0.72	0.58
P₄	0.56	0.37	0.32
P₅	0.39	0.27	0.24
P₆	0.29	0.24	0.20

Indoor Positioning Algorithm Based on WiFi-BP

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