A recursive NLOS bias estimation and correction algorithm

Research output: Contribution to journalConference articlepeer-review


The importance of the indoor positioning applications and services in many fields such as health and safety motivated the researchers to develop accurate and cost effective localization systems. In wireless positioning techniques, the position of the mobile node (MN) can be estimated by measuring the distances between the MN and the access points (APs) using ranging techniques such as Time-of-Arrival (TOA), Time-Difference-of-Arrival (TDOA) and Received Signal Strength (RSS). However, due to dense indoor environments, multipath propagation and Non-Line-of-Sight (NLOS) introduce biases to the range measurements causing inaccurate position estimation. This paper proposes a recursive NLOS bias estimator algorithm, which corrects the range measurements by removing the estimated biases. The proposed algorithm is non-parametric and it doesn’t require a priori information about the environment. Simulation results show that the proposed algorithm has higher positioning accuracy compared to the other state of the art algorithms and it outperforms them by at least 137%.

Original languageBritish English
Pages (from-to)251-258
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2019
EventShort Paper of the 10th International Conference on Indoor Positioning and Indoor Navigation - Work-in-Progress Papers, IPIN-WiP 2019 - Pisa, Italy
Duration: 30 Sep 20193 Oct 2019


  • Bias Correction
  • Indoor Positioning
  • NLOS
  • NLOS mitigation


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