Accurate indoor localization with multiple feature fusion

Yalong Xiao, Jianxin Wang, Shigeng Zhang, Haodong Wang, Jiannong Cao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


In recent years, many fingerprint-based localization approaches have been proposed, in which different features (e.g., received signal strength (RSS) and channel state information (CSI)) were used as the fingerprints to distinguish different positions. Although CSI-based approaches usually achieve higher accuracy than RSSI-based approaches, we find that the localization results of different approaches usually com-pensate with each other, and by fusing different features we can get more accurate localization results than using only single feature. In this paper, we propose a localization method that fusing different features by combining results of different localization approaches to achieve higher accuracy. We first select three most possible candidate positions from all the candidate positions generated by different approaches according to a newly defined metric called confidence degree, and then use the weighted average of them as the position estimation. When there are more than three candidate positions, we use a minimal-triangle principle to break the tie and select three out of them. Our experiments show that the proposed approach achieves median error of 0.5 m and 1.1 m respec-tively in two typical indoor environments, significantly better than that of approaches using only single feature.
Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications - 12th International Conference, WASA 2017, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319600321
Publication statusPublished - 1 Jan 2017
Event12th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2017 - Guilin, China
Duration: 19 Jun 201721 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10251 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2017

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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