Drone Classification Experiments by University of Louisville
In Fall 2020, the AERPAW team hosted researchers from the University of Louisville (UoL) to carry out experiments in the Lake Wheeler site related to drone detection and classification. Olusiji Medaiyese from UoL visited NC State University and worked with AERPAW pilots to collect data from various different drones and other RF sources using a high sampling oscilloscope from Keysight that was made available to them for their experiments. After post-processing the data collected from the Lake Wheeler site, Medaiyese submitted the following journal:
- Olusiji Medaiyese, Martins Ezuma, Adrian P. Lauf, and Ismail Guvenc, “Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning”, submitted to Elsevier Journal of Pervasive and Mobile Computing, Feb. 2021 [Paper].
The UoL team will make the collected data publicly available in the future and cited it in their accepted journal paper. An example drone dataset that was posted by the AERPAW team in the past is available at [Dataset].
This experiment fits under bring your own device (BYOD) type AERPAW experiments. AERPAW is expected to be “generally available” in the coming months and will be broadly accessible to experimenters with programmable drones and programmable wireless equipment. On a case-by-case basis, the AERPAW team can also work with the interested researchers on BYOD-type experiments.
Drone and Rover Experiments
We have been testing our AERPAW UAV and rover in the Lake Wheeler site, both of which have the same interface for controlling their trajectory. Below you can find a video from our recent testing, where the rover is controlled in manual control (the pilot has control), automatic control (autopilot has control), as well as autonomous control (the portable node has control). All tests went well and we are getting ready for supporting external experiments with AERPAW UAVs and rovers in the coming months.
