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Airborne Radar-Based Detection and Discrimination of Small Unmanned Aerial Systems and Birds For Collision Avoidance and Force Protection


RT&L FOCUS AREA(S): Autonomy; General Warfighting Requirements

TECHNOLOGY AREA(S): Air Platforms; Electronics

OBJECTIVE: Develop and demonstrate advanced airborne radar modes for the detection and discrimination of small Unmanned Aerial Systems (sUAS) and birds.

DESCRIPTION: When used in a nefarious manner, sUAS (DoD groups 1-3) can pose a threat to both civil and military activities [Ref 1]. However, like birds [Ref 2], sUAS, even when operated in a legal manner, can pose a collision threat for other aircraft. From a force protection perspective (e.g., providing advanced warning of sUAS operating in the vicinity of USN ships), the detection and discrimination of sUAS at ranges (on the order of one nautical mile) sufficient to employ countermeasures are needed. From an aviation safety perspective, detection and discrimination of birds and sUAS are needed at ranges sufficient to determine and execute appropriate collision avoidance maneuvers (which varies as a function of encounter speed and UAS maneuverability). While a truly robust solution will likely leverage multiple sensor modalities, radar is the first line of defense from manned and unmanned aircraft. Radar detection of sUAS and birds (either larger individual birds or flocks of smaller birds) is very challenging due to their very low backscattering radar cross sections (RCS) [Refs 3-4]. Particularly for low-altitude airborne operations overland and over water, these low-RCS returns can be buried in surface clutter returns. Techniques to separate clutter and target signatures are required. Discrimination of sUAS and birds can in part be informed by the track kinematics. Ultimately, discrimination will leverage subtle signatures contained within the radar return that are generated by wing movement of birds or movements of surfaces (e.g., propellers) on sUAS. These micro-Doppler signatures have been investigated by multiple authors [Refs 5-11]. Developing an operationally suitable airborne bird and sUAS detection and discrimination capability for Navy fixed and rotary wing aircraft radar systems requires thorough study. Candidate radar systems range from UAS collision avoidance radars like the ZPY-9, maritime surveillance radars like the ZPY-8 and APS-153, and fire control radars like the APG-79 and APG-81. These radar systems and their host platforms are all dramatically different. Constraining the surveillance volume and balancing the radar timeline for this mode with other mission requirements are required. Not all radar/platform combinations will be able to provide an operationally suitable capability.

PHASE I: Identify candidate radar mode designs for conceptual radar systems representative of the Navy radar systems listed in the Description. Explore innovative approaches to maximize capability from various radar architectures through comprehensive analysis with no requirement for Navy-specific radar design information. Provide a detailed detection and discrimination algorithm description and analysis as implemented on the range of conceptual radar systems. The analyses should be sufficiently comprehensive so as to inform operational feasibility and the Phase II focus. Develop a Phase II plan.

PHASE II: Select candidate radar system(s) as the basis for detailed detection and discrimination mode development. Perform high fidelity radar, target and environmental simulation. Produce a detailed airborne radar collection and analysis plan. Assess hardware, software and firmware impacts to accommodate the new radar mode. Provide software source code and executable files, draft system/subsystem specification updates, and draft performance specification inputs.

PHASE III DUAL USE APPLICATIONS: Integrate into existing Military sense and avoid radar systems and comparable commercial UAS systems, as well as radar systems (e.g., weather radar systems) used in manned private and commercial passenger aircraft.


  1. Lacher, Andrew; Baron, Jonathan; Rotner, Jonathan; and Balazs, Michael. “Small Unmanned Aircraft: Characterizing the Threat.” The Mitre Corporattion, 2019.  
  2. Federal Aviation Administration. “Wildlife Hazard Mitigation.
  3. Torvik, BØerge; Olsen, Karl Erick; and Griffiths, Hugh. “K-band radar signature analysis of a flying mallard duck.” IEEE: 2013 14th International Radar Symposium (IRS), 19-21 June 2013.
  4. Ritchie, Matthew; Fioranelli, Francesco; Griffiths, Hugh; and Torvik, BØrge. “Monostatic and bistatic radar measurements of birds and micro-drone.” 2016 IEEE Radar Conference (RadarConf).
  5. Molchanov, P.; Egiazarian, K.; Astola, J.; Harmanny, R.I.A.; and de Wit, J.J.M. “Classification of small UAVs and birds by micro-Doppler signatures.” 2013 European Radar Conference.
  6. De Wit, J. J. M,; Harmanny, R. I. A.; and Molchanov, P. “Radar micro-Doppler feature extraction using the Singular Value Decomposition”. 2014 IEEE International Radar Conference.
  7. De Wit, Jacco J. M.; van Dorp, Philip; and Huizing, Albert G. “Classification of air targets based on range-Doppler diagrams.” 2016 European Radar Conference.  
  8. Jahangir, Mohammed; Baker, Chris J.; and Oswald, Gordon A. “Doppler characteristics of micro-drones with L-Band multibeam staring radar.” 2017 IEEE Radar Conference (RadarConf).  
  9. Rahman, Samiur and Robertson, Duncan. “Time-Frequency Analysis of Millimeter-Wave Radar Micro-Doppler Data from Small UAVs.” 2017 Sensor Signal Processing for Defence (SSPD) Conference.  
  10. Avbar, Bahadir and Yilmaz, Ali Özgür. “Micro-Doppler analysis of rotary-wing air vehicles using pulsed-Doppler radar.” 2018 26th Signal Processing and Communications Applications Conference (SIU).
  11. 11. Björklund, Svante. “Target Detection and Classification of Small Drones by Boosting on Radar Micro-Doppler.” 2018 15th European Radar Conference.
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