RSSI-Based Supervised Learning for Uncooperative Direction-Finding

  • Tathagata MukherjeeEmail author
  • Michael Duckett
  • Piyush Kumar
  • Jared Devin Paquet
  • Daniel Rodriguez
  • Mallory Haulcomb
  • Kevin George
  • Eduardo Pasiliao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)


This paper studies supervised learning algorithms for the problem of uncooperative direction finding of a radio emitter using the received signal strength indicator (RSSI) from a rotating and uncharacterized antenna. Radio Direction Finding (RDF) is the task of finding the direction of a radio frequency emitter from which the received signal was transmitted, using a single receiver. We study the accuracy of radio direction finding for the 2.4 GHz WiFi band, and restrict ourselves to applying supervised learning algorithms for RSSI information analysis. We designed and built a hardware prototype for data acquisition using off-the-shelf hardware. During the course of our experiments, we collected more than three million RSSI values. We show that we can reliably predict the bearing of the transmitter with an error bounded by 11\(^\circ \), in both indoor and outdoor environments. We do not explicitly model the multi-path, that inevitably arises in such situations and hence one of the major challenges that we faced in this work is that of automatically compensating for the multi-path and hence the associated noise in the acquired data.


Data mining Radio direction finding Software defined radio Regression GNURadio Feature engineering 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tathagata Mukherjee
    • 2
    Email author
  • Michael Duckett
    • 1
  • Piyush Kumar
    • 1
  • Jared Devin Paquet
    • 4
  • Daniel Rodriguez
    • 1
  • Mallory Haulcomb
    • 1
  • Kevin George
    • 2
  • Eduardo Pasiliao
    • 3
  1. 1.CompGeom Inc.TallahasseeUSA
  2. 2.Intelligent Robotics Inc.TallahasseeUSA
  3. 3.Munitions DirectorateAFRLEglin AFBUSA
  4. 4.REEFShalimarUSA

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