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Geometric Indoor Radiolocation: History, Trends and Open Issues

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Machine Learning for Indoor Localization and Navigation

Abstract

In this chapter we discuss some basic approaches for indoor positioning system definition and, in particular, those based on the electromagnetic properties of the received signal, the so-called geometric radiolocation techniques. A brief reference to the localization history, actors, and architectures, along of a taxonomy of the different concepts of localization, introduces the chapter, before presenting a detailed discussion of the most common techniques. The chapter also evidences the issues associated to the indoor propagation environment, in order to understand the efforts made by the scientific community on mitigating the artifacts of indoor localization systems. To complete this discussion, we analyze the role that machine learning and artificial intelligence play in the indoor positioning problem solution.

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Authors and Affiliations

  1. Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

    Antonello Florio, Gianfranco Avitabile & Giuseppe Coviello

Authors
  1. Antonello Florio
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  2. Gianfranco Avitabile
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  3. Giuseppe Coviello
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Corresponding author

Correspondence to Giuseppe Coviello .

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Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Saideep Tiku

  2. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

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Florio, A., Avitabile, G., Coviello, G. (2023). Geometric Indoor Radiolocation: History, Trends and Open Issues. In: Tiku, S., Pasricha, S. (eds) Machine Learning for Indoor Localization and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-031-26712-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-26712-3_3

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  • Print ISBN: 978-3-031-26711-6

  • Online ISBN: 978-3-031-26712-3

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