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Sensors 2015, 15(9), 21824-21843;

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion

School of Software, Beijing Institute of Technology, Haidian District, Beijing 100081, China
Author to whom correspondence should be addressed.
Academic Editors: Kourosh Khoshelham and Sisi Zlatanova
Received: 5 July 2015 / Revised: 15 August 2015 / Accepted: 27 August 2015 / Published: 31 August 2015
(This article belongs to the Special Issue Sensors for Indoor Mapping and Navigation)
Full-Text   |   PDF [766 KB, uploaded 2 September 2015]   |  


The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy. View Full-Text
Keywords: WiFi indoor positioning; location fingerprinting algorithm; weighted fusion WiFi indoor positioning; location fingerprinting algorithm; weighted fusion

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Ma, R.; Guo, Q.; Hu, C.; Xue, J. An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion. Sensors 2015, 15, 21824-21843.

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