Spatial Aware Signal Space Clustering algorithm for optimal calibration point locations in location fingerprinting

Chamal Sapumohotti, Mohamad Yusoff Alias, Tan Su Wei

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

3 Citations (Scopus)


Location fingerprinting provides localization for devices in indoor environments using existing Wireless Local Area Network (WLAN) infrastructure. However, the initial offline calibration which is required for these types of systems is non-trivial and requires significant labour cost. The current practice is to collect measurements at calibration points in a uniform grid for the area which is covered by the radio map. However, not all calibration points are resolvable in signal space and addition of unresolvable calibration points does not improve the localization accuracy. This paper presents Spatial Aware Signal Space Clustering (S3C) clustering algorithm which analyses walk test data for identifying these unresolvable calibration points prior to calibration phase. Simulation based studies shows that the algorithm is able to reduce the labour cost of calibration phase while preserving the localization accuracy.

Original languageEnglish
Title of host publication2013 19th Asia-Pacific Conference on Communications, APCC 2013
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)9781467360500
Publication statusPublished - 2013
Externally publishedYes
EventAsia Pacific Conference on Communications 2013 - Bali Dynasty Resort, Denpasar, Indonesia
Duration: 29 Aug 201331 Aug 2013
Conference number: 19th


ConferenceAsia Pacific Conference on Communications 2013
Abbreviated titleAPCC 2013


  • clustering
  • Location fingerprinting
  • reducing calibration points
  • signal space separation

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