Scenario-5: Active Fixed Passive Mobile (AFPM)

This experimental scenario considers that there is a PAW (e.g. an SDR, radar, RF sensor, etc.) at the fixed node while there is no PAW at the drone or other vehicles. For example, the SDR at the fixed node may passively sense the signals from the environment (such as the drone control signals) or it can use radar signals (Fortem, UWB) to track the drone. The drone may be piloted by AERPAW, or it may be a drone not owned/piloted by AERPAW.

As an example of possible experiments in this scenario, experimenters can use Keysight RF sensors or USRPs to capture drone control/payload signals radiated from the drone, and using those, can detect/classify/localize/track the drone. There are a few recent incidents of micro-UAVs violating public privacy and the security of sensitive facilities such as nuclear power plants and airports. Interestingly, most of these events occurred when drone pilots intentionally violated no-fly-zone restrictions. Hence, it is of critical importance to identify such unconventional threats. This can be achieved by accurately detecting and identifying non-compliant commercial small drones. In addition to using passive RF signals to detect/classify/localize/track the drones, we will also support the use of active radar transmissions (e.g. Fortem or UWB radars) for tracking drones.

RF Sensing of Drones

[1] M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, “Micro-UAV detection and classification from RF fingerprints using machine learning techniques,” in Proc. IEEE Aerospace Conf., Big Sky, MT, Mar. 2019.
[2] M. Ezuma, I. Guvenc, W. A. G. Khawaja, O. Ozdemir, and C. K. Anjinappa, “Micro-UAV detection with a low-grazing angle millimeter-wave radar,” in Proc. IEEE Radio Wireless Symp., Orlando, FL, Jan. 2019.
[3] M. Ezuma, F. Erden, C. Kumar Anjinappa, O. Ozdemir, and I. Guvenc, “Detection and classification of UAVs using RFfingerprints in the presence of wi-fi and bluetooth interference,” IEEE Open J. Commun. Soc., vol. 1, pp. 60–76, 2020

Radar Sensing of Drones

[1] M. Ritchie, “Multistatic micro-doppler radar feature extraction for classification of unloaded/loaded microdrones,” IET Radar, Sonar and Navigation, vol. 11, pp. 116–124(8), Jan 2017.
[2] M. Jahangir, C. J. Baker, and G. A. Oswald, “Doppler characteristics of micro-drones with L-band multibeam staring radar,” in 2017 IEEE Radar Conf. (RadarConf), 2017, pp. 1052–1057.
[3] P. Molchanov, R. I. Harmanny, J. J. de Wit, K. Egiazarian, and J. Astola, “Classification of small UAVs and birds by micro-doppler signatures,” Int. J. Microwave Wireless Technol., vol. 6, no. 3-4, p. 435–444, 2014.

Sensing of Vehicles and People

[1] M. Bocca, O. Kaltiokallio, N. Patwari, and S. Venkatasubramanian, “Multiple target tracking with RF sensor networks,” IEEE Trans. Mobile Comput., vol. 13, no. 8, pp. 1787–1800, 2014.
[2] N. Kassem, A. E. Kosba, and M. Youssef, “RF-based vehicle detection and speed estimation,” in Proc. IEEE Veh. Technol. Conf. (VTC Spring), 2012, pp. 1–5.
[3] W. Khawaja, F. Koohifar, and I. Guvenc, “UWB radar based beyond wall sensing and tracking for ambient assisted living,” in Proc. IEEE Consumer Commun. & Netw. Conf. (CCNC), 2017, pp. 142–147.
[4] S. Jeng, W. Chieng, and H. Lu, “Estimating speed using a side-looking single-radar vehicle detector,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 607–614, 2014.

Spectrum Occupancy Monitoring

[1] A. Ali, N. Gonzalez-Prelcic, R. W. Heath, and A. Ghosh, “Leveraging sensing at the infrastructure for mmWave communication,” IEEE Commun. Mag., vol. 58, no. 7, pp. 84–89, 2020.
[2] A. Al-Hourani, V. Trajkovic, S. Chandrasekharan, and S. Kandeepan, “Spectrum occupancy measurements for different urban environments,” in European Conf. Netw. Commun. (EuCNC), 2015, pp. 97–102.
[3] D. Cabric, A. Tkachenko, and R. W. Brodersen, “Experimental study of spectrum sensing based on energy detection and network cooperation,” ser. TAPAS ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 12–es. [Online]. Available: