Domain clustering based WiFi indoor positioning algorithm

Wei Zhang, Xianghong Hua, Kegen Yu, Weining Qiu, Shoujian Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

21 Citations (Scopus)


This paper focuses on WiFi indoor positioning based on received signal strength, a common local positioning approach with a number of prominent advantages such as low cost and ease of deployment. Weighted k nearest neighbor (WKNN) approach and Naive Bayes Classifier (NBC) method are two classic position estimation strategies for location determination using WiFi fingerprinting. Both of them need to handle carefully the issue of access point (AP) selection and inappropriate selection of APs may degrade positioning performance considerably. To avoid the issue of AP selection and hence improve positioning accuracy, a new WiFi indoor position estimation strategy via domain clustering (DC) is proposed in this paper. Extensive experiments are carried out and performance comparison based on experimental results demonstrates that the proposed method has a better position estimation performance than the existing approaches.

Original languageEnglish
Title of host publicationIPIN 2016 : International Conference on Indoor Positioning and Indoor Navigation : proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781509024254
Publication statusPublished - 14 Nov 2016
Externally publishedYes
Event2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016 - Madrid, Spain
Duration: 4 Oct 20167 Oct 2016


Other2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016


  • Domain clustering
  • Naive Bayes Classifier
  • Weighted k nearest neighbor
  • WiFi indoor positioning techniques


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