Hierarchical Learning For FM Radio Based Aerial Localization Using RSSI

  • Tathagata Mukherjee Intelligent Robotics Inc
  • Andreas Adolfsson Intelligent Robotics, Inc
  • Eduardo Jr L Pasiliao Air Force Research Laboratory
  • Piyush Kumar CompGeom Inc

Abstract

Received Signal Strength Indicator~(RSSI) based large scale positioning systems are beginning to gain traction as coarse positioning systems when GPS is unavailable. In this paper we present a system for automatic positioning of an unmanned aerial system using broadcast FM radio. Our method is data driven, and uses machine learning techniques to improve its accuracy. The techniques are easy to extend to other terrestrial static radio transmitters. Using our algorithms, we can localize with a minimum error of 172 meters and mean error less than 3000 meters.

Published
2017-09-05
How to Cite
MUKHERJEE, Tathagata et al. Hierarchical Learning For FM Radio Based Aerial Localization Using RSSI. Proceedings of the GNU Radio Conference, [S.l.], v. 2, n. 1, p. 8, sep. 2017. Available at: <https://pubs.gnuradio.org/index.php/grcon/article/view/28>. Date accessed: 23 nov. 2017.