Robust Detection of Micro-UAS Drones with L-Band 3-D Holographic Radar

Abstract

Unmanned Aerial Systems (UAS) are pilotless aircraft (drone) and are characterized by having very small radar cross-sections, relatively slow motion profiles and low operating altitudes compared with manned aircraft. As a direct consequence they are considerably more difficult to detect and track. This is exacerbated in traditional 2-D scanning radar which struggle to find a compromise between the conflicting needs to simultaneously have short re-visit times and high Doppler resolution. Here, we use Holographic RadarTM (HR) that employs a 2-D antenna array and appropriate signal processing to create a multibeam, 3-D, wide-area, staring surveillance sensor capable of achieving high detection sensitivity, whilst providing fine Doppler resolution with update rates of fractions of a second. The ability to continuously dwell on targets over the entire search volume enables HR to achieve a level of processing gain sufficient for detection of very low signature targets such as miniature UAS against a background of complex stationary and moving clutter. In this paper trials results are presented showing detection of a small hexacopter UAS using a 32 by 8 element L- Band receiver array. The necessary high detection sensitivity means that many other small moving targets are detected and tracked, birds being a principle source of clutter. To overcome this a further stage of processing is required to discriminate the UAS from other moving objects. Here, a machine learning decision tree classifier is used to reject non- drone targets resulting in near complete suppression of false tracks whilst maintaining a high probability of detection for the drone. Unmanned Aerial Systems (UAS) are pilotless aircraft (drone) and are characterized by having very small radar cross-sections, relatively slow motion profiles and low operating altitudes compared with manned aircraft. As a direct consequence they are considerably more difficult to detect and track. This is exacerbated in traditional 2-D scanning radar which struggle to find a compromise between the conflicting needs to simultaneously have short re-visit times and high Doppler resolution. Here, we use Holographic RadarTM (HR) that employs a 2-D antenna array and appropriate signal processing to create a multibeam, 3-D, wide-area, staring surveillance sensor capable of achieving high detection sensitivity, whilst providing fine Doppler resolution with update rates of fractions of a second. The ability to continuously dwell on targets over the entire search volume enables HR to achieve a level of processing gain sufficient for detection of very low signature targets such as miniature UAS against a background of complex stationary and moving clutter. In this paper trials results are presented showing detection of a small hexacopter UAS using a 32 by 8 element L- Band receiver array. The necessary high detection sensitivity means that many other small moving targets are detected and tracked, birds being a principle source of clutter. To overcome this a further stage of processing is required to discriminate the UAS from other moving objects. Here, a machine learning decision tree classifier is used to reject non- drone targets resulting in near complete suppression of false tracks whilst maintaining a high probability of detection for the drone.