Detection and Identification of Remotely Piloted Aircraft Systems Using Weather Radar

The Federal Aviation Administration (FAA) forecasts more than 600,000 remotely piloted aircraft systems (RPAS) will be in the skies by September 2017 following new regulations allowing routine use of drones in the national airspace. While the increase in sales is expected to continue, the number of incidents involving drones has increased exponentially around the world. According to data compiled in the FAA national drone database, there have been 474 sightings, near misses, and other reported issues involving drones between July and September 2016 only. 

The researchers from University of Massachusetts and Olin College have demonstrated the RPAS detection and identification capabilities using a high power (Umass X-Pol) and a low-power (Umass Phase-Spin Weather Radar) X-band radars.

The challenge in the detection of a very small target that flies low is that it can be obscured in ground clutter from trees, buildings etc. When the RPAS is moving, standard moving target detection techniques (such as Moving Target Detector) can work well. It is when the RPAS is hovering or moving at near zero Doppler velocity in a high clutter region that detection can be difficult. A key finding, also observed by others (Fioranelli et al, 2015; Harmanny et al, 2014), is that copter type RPAS has a characteristic Doppler spectrum. An example of a micro-Doppler signature consisting of a central peak due to return from the drone body surrounded on either side by a set of weaker spectral lines due to the spinning blades is shown in Figure 1. This signature allows for a reliable differentiation between birds and drones, which exhibit comparable radar cross sections. A performance of the automatic drone detection method based on micro-Doppler signature will be evaluated. This paper will demonstrate RPAS detection and identification capabilities in both stationary and moving scenarios. Additionally, dual polarization products will be presented. 

Fioranelli, F., Ritchie, M., Griffiths, H. & Borrion, H. (2015). "Classification of loaded/unloaded micro-drones using multistatic radar." Electronics Letters 51, no. 22, 1813-1815.

Harmanny, R. I. A., M. de Wit, J.J., & Cabic P. G. (2014). "Radar micro-Doppler feature extraction using the spectrogram and the cepstrogram," In European Radar Conference (EuRAD), 2014 11th, pp. 165-168. IEEE.