RSS-Based Indoor Positioning Based on Multi-Dimensional Kernel Modeling and Weighted Average Tracking

Ching-Chun Huang, Hung Nguyen Manh

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


In this paper, we discuss a similarity inconsistency phenomenon where the radio signal strength (RSS) signatures of two neighboring positions are dissimilar due to the RSS variation. While matching an observed RSS throughout the radio map, the phenomenon would lead to a jagged similarity distribution. This may break the similarity assumption of the previous works. To address the problem, we proposed a multi-dimensional kernel density estimation (MDKDE) method. By introducing the spatial kernel, the method could adopt neighboring information to enrich the fingerprint. The model can also help to generate a smooth and consistent similarity distribution. Moreover, we formulated the searching of the target location over the continuous domain as an optimization problem. Instead of estimating the optimal location numerically, we also came up with an efficient tracking method, weighted average tracker (WAT). Upon the MDKDE model, WAT can track the target in a simple weighted average method. The experimental results have demonstrated that the proposed system could well model the RSS variation and provide robust positioning performance in an efficient manner.

Original languageEnglish
Article number7397867
Pages (from-to)3231-3245
Number of pages15
JournalIEEE Sensors Journal
Issue number9
StatePublished - 1 May 2016


  • Indoor Localization
  • Multi-dimensional Kernel Density Estimation
  • Multi-modal Distribution
  • Radio Fingerprint
  • Similarity Inconsistency
  • Weighted Average Tracker

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