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3D Localization and Frequency Band Estimation of Multiple Unknown RF Sources Using Particle Filters and a Wireless Sensor Network

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Abstract

This paper presents a methodology for the 3D localization and the frequency band estimation of multiple unknown RF sources, using received signal strength (RSS) measurements from a wireless network of static sensors and a moving sensor. Using a particle filter based Bayesian approach, the static sensor network is used to continuously monitor an urban area and provide an estimate of the probability distribution for the states of the unknown sources. Based on this probability distribution, a moving sensor is guided towards locations where it is more likely to find an unknown source. The RSS measurements in the vicinity of the unknown source are used to provide the final estimation and confirmation of the source state. The method is analytically developed and evaluated in terms of accuracy in estimating the number, the 3D location and the frequency band of the unknown sources under different Monte Carlo simulation scenarios.

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Authors and Affiliations

  1. Department of Physics, Aristotle University of Thessaloniki, 54124, Thessaloníki, Greece

    Konstantinos A. Gotsis

  2. Department of Electrical and Computer Engineering, University of Nicosia, 46 Makedonitissas Avenue, Nicosia, Cyprus

    Ioannis Kyriakides & John N. Sahalos

Authors
  1. Konstantinos A. Gotsis
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  2. Ioannis Kyriakides
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  3. John N. Sahalos
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Correspondence to Konstantinos A. Gotsis.

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Gotsis, K.A., Kyriakides, I. & Sahalos, J.N. 3D Localization and Frequency Band Estimation of Multiple Unknown RF Sources Using Particle Filters and a Wireless Sensor Network. Wireless Pers Commun 90, 1889–1902 (2016). https://doi.org/10.1007/s11277-016-3429-z

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  • DOI: https://doi.org/10.1007/s11277-016-3429-z

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