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Positioning Based on Noise-Limited Censored Path Loss Data

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


Original languageEnglish
Title of host publication2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings
EditorsJari Nurmi, Elena-Simona Lohan, Joaquin Torres-Sospedra, Heidi Kuusniemi, Aleksandr Ometov
Number of pages6
ISBN (Electronic)9781728164557
Publication statusPublished - Jun 2020
Publication typeA4 Article in a conference publication
EventInternational Conference on Localization and GNSS -
Duration: 2 Jun 20204 Jun 2020

Publication series

Name2020 International Conference on Localization and GNSS, ICL-GNSS 2020 - Proceedings


ConferenceInternational Conference on Localization and GNSS


Positioning is considered one of the most important features and enabler of various novel industry verticals in future radio systems. Since path loss or received signal strength-based measurements are widely available and accessible in the majority of wireless standards, path loss-based positioning has an important role among other positioning technologies. Conventionally path loss-based positioning has two phases; i) fitting a path loss model to training data, if such training data is available, and ii) determining link distance estimates based on the path loss model and calculating the position estimate. However, in both phases, the maximum measurable path loss is limited by measurement noise. Such immeasurable samples are called censored path loss data and such noisy data is commonly neglected in both the model fitting and in the positioning phase. In the case of censored path loss, the loss is known to be above a known threshold level and that information can be used in model fitting as well as in the positioning phase. In this paper, we examine and propose how to use censored path loss data in path loss model-based positioning and demonstrate with simulations the potential of the proposed approach for considerable improvements (over 30%) in positioning accuracy.


  • censored data, localization, maximum-likelihood estimation, path loss, path loss model, positioning, probabilistic modeling., shadow fading, wireless networks

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