Research Output
Simultaneous context inference and mapping using mm-Wave for indoor scenarios
  We introduce in this paper two main approaches, Triangulateration (TL) and Angle-Difference-of-Arrival (ADoA) for indoor localization and mapping using single-anchor and millimeter wave (MMW) propagation characteristics. Then, we perform context inference through obstacle localization. To do so, we first include and estimate the positions of virtual anchor nodes (VANs), known as mirrors of the real anchor with respect to obstacle. Then, it is followed by estimating the obstacle position and its dimensions. We assess the performance of each technique via cumulative distribution function (CDF) for the location estimation root mean square error (RMSE). Simulations confirm that localization of the receiver relying on a single anchor and the localization of obstacles in MMW achieves a few centimeters accuracy

  • Date:

    31 July 2017

  • Publication Status:

    Published

  • DOI:

    10.1109/icc.2017.7996976

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Edinburgh Napier Funded

Citation

Yassin, A., Nasser, Y., Awad, M., & Al-Dubai, A. (2017). Simultaneous context inference and mapping using mm-Wave for indoor scenarios. In IEEE ICC 2017, (1-6). https://doi.org/10.1109/icc.2017.7996976

Authors

Keywords

Millimeter wave, Triangulateration (TL), Angle-Difference-of-Arrival (ADoA), virtual anchor node (VAN), obstacle detection

Monthly Views:

Available Documents