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Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios

Author

Listed:
  • Antonio Del Corte-Valiente

    (Department of Computer Engineering, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • José Manuel Gómez-Pulido

    (Department of Computer Engineering, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • Oscar Gutiérrez-Blanco

    (Department of Computer Engineering, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

  • José Luis Castillo-Sequera

    (Department of Computer Engineering, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain)

Abstract

The increase of the technology related to radio localization and the exponential rise in the data traffic demanded requires a large number of base stations to be installed. This increase in the base stations density also causes a sharp rise in energy consumption of cellular networks. Consequently, energy saving and cost reduction is a significant factor for network operators in the development of future localization networks. In this paper, a localization method based on ray-tracing and fingerprinting techniques is presented. Simulation tools based on high frequencies are used to characterize the channel propagation and to obtain the ray-tracing data. Moreover, the fingerprinting technique requires a costly initial learning phase for cell fingerprint generation (radio-map). To estimate the localization of mobile stations, this paper compares power levels and delay between rays as cost function with different distance metrics. The experimental results show that greater accuracy can be obtained in the location process using the delay between rays as a cost function and the Mahalanobis distance as a metric instead of traditional methods based on power levels and the Euclidean distance. The proposed method appears well suited for localization systems applied to indoor and outdoor scenarios and avoids large and costly measurement campaigns.

Suggested Citation

  • Antonio Del Corte-Valiente & José Manuel Gómez-Pulido & Oscar Gutiérrez-Blanco & José Luis Castillo-Sequera, 2019. "Localization Approach Based on Ray-Tracing Simulations and Fingerprinting Techniques for Indoor–Outdoor Scenarios," Energies, MDPI, vol. 12(15), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2943-:d:253368
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    References listed on IDEAS

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    1. Michiel Aernouts & Rafael Berkvens & Koen Van Vlaenderen & Maarten Weyn, 2018. "Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas," Data, MDPI, vol. 3(2), pages 1-15, April.
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    Cited by:

    1. Yaning Li & Hongsheng Li & Baoguo Yu & Jun Li, 2022. "A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN," Future Internet, MDPI, vol. 14(8), pages 1-18, July.

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