Next Article in Journal
An Insertable Passive LC Pressure Sensor Based on an Alumina Ceramic for In Situ Pressure Sensing in High-Temperature Environments
Next Article in Special Issue
INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm
Previous Article in Journal
Initial Alignment of Large Azimuth Misalignment Angles in SINS Based on Adaptive UPF
Previous Article in Special Issue
A Probabilistic Feature Map-Based Localization System Using a Monocular Camera
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 21824-21843; doi:10.3390/s150921824

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)
View Full-Text   |   Download PDF [766 KB, uploaded 2 September 2015]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top