Enhanced Conformal Predictors for Indoor Localisation Based on Fingerprinting Method

  • Khuong An Nguyen
  • Zhiyuan Luo
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)


We proposed the first Conformal Prediction (CP) algorithm 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 show 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.


indoor localisation fingerprinting conformal prediction 


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Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Khuong An Nguyen
    • 1
  • Zhiyuan Luo
    • 1
  1. 1.Dept. of Computer ScienceRoyal Holloway, University of LondonEghamUnited Kingdom

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