Enhanced Conformal Predictors for Indoor Localisation Based on Fingerprinting Method

Khuong Nguyen, Zhiyuan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We proposed the first Conformal Prediction algorithm (CP) for indoor localisation with a classification approach. The algorithm can provide a region of predicted locations, and a reliability measurement for each prediction. However, one of the shortcomings of the former approach was the individual treatment of each dimension. In reality, the training database usually contains multiple signal readings at each location, which can be used to improve the prediction accuracy. In this paper, we enhance our former CP with the Kullback-Leibler divergence, and propose two new classification CPs. The empirical studies showed that our new CPs performed slightly better than the previous CP when the resolution and density of the training database are high. However, the new CPs performs much better than the old CP when the resolution and density are low.
Original languageEnglish
Title of host publication9th IFIP International Conference on Artificial Intelligence Applications & Innovations (AIAI 2013)
Number of pages10
ISBN (Electronic)978-3-642-41142-7
Publication statusPublished - 2 Oct 2013

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer Berlin Heidelberg


  • indoor localisation
  • location fingerprinting
  • conformal prediction

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