Student Work

Drone Localization and Jamming Assembly


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With the cost of drones decreasing and their presence in the world increasing, there needs to be a way to control their presence in restricted airspace. To solve this problem, our team designed an assembly to both locate an unwanted drone or drone pilot and jam the communication between drone and the drone controller. First the location was discovered using several SDRs to record signal strength at different locations. This would find either the drone or drone controller when other signals were not present on the same bandwidth. The SDR signal data was then processed to get the signal’s received signal strength (RSS) These results were then mathematically modeled with the path loss model, which turned the RSS values into a distance in meters. Then the recursive least squares algorithm transformed the distances from each SDR into a position in the space that corresponds to the drone or drone controller. Finally, the Cramer Rao Lower Bound was used to estimate the amount of error possible in the position estimation. The jamming was achieved using a microcontroller to broadcast white Gaussian noise at the same frequency as the drone communication to render that communication useless. The location determining system was found to be within a meter of error on the xy plane, however, it had more error on the z axis as the SDRs were not at different enough z positions to sense the z position as accurately. The jamming system was designed and built using analog RF components, fed by voltages from the microcontroller. The effectiveness of the jamming system could not be fully tested as strict COVID-19 protocols limited access to diagnostic equipment.

  • This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
  • E-project-121020-173844
  • 4881
  • 2020
UN Sustainable Development Goals
Date created
  • 2020-12-10
Resource type
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