IEEE - Aerospace and Electronic Systems - September 2023 - 40

Radar Challenges, Current Solutions, and Future Advancements for the Counter Unmanned Aerial Systems Mission
Figure 5.
Multipath illustration shows the direct and indirect paths for target reflections. The indirect path dynamically adds constructively and
destructively altering the estimated elevation angle measurement of the target. For C-UAS radars, this is primarily main beam multipath at
elevation angles relative to the radar of less than 10.
max speeds similar to sUAS (20-100 m/s) and have a radar
cross section (RCS) that is also comparable to drones (25 to
20 dBsm). This creates a source of ambiguity for the radar
tracker and can lead to false positive and negative classifications
due to birds (see Figure 8). For C-UAS radar modes that
simultaneously detect Group 1/2 and Group 3-5 UAS, this
poses a problem. For larger targets that fly at speeds much
greater than birds, classification is minimally impacted. However,
if the requirements necessitate tracking slower moving
Group 1 and 2 UAS, the improper classification of birds as
UAS will clutter the air picture and degrade battlespace
awareness. For C-UAS applications that focus only on Group
1/2 UAS, classification becomes a larger problem.
EXISTINGSOLUTIONS
The most prevalent solution for mitigating false bird detections
is to couple the radar with an EO/IR sensor. The radar
generates target tracks that are passed to a multispectral camera.
The camera is cued in the direction of the radar track,
and visual confirmation is used to verify ifthe track is a valid
drone detection or not. Additionally, artificial intelligence/
machine learning (AI/ML) is used for target classification.
AI/ML classification algorithms for imagery are well understood
and mature [40], [41]. These algorithms are applied to
the images captured by an EO/IR sensor that is coupled with
a radar in C-UAS. Autotracking cameras with AI/ML classification
capability are a powerful mitigation solution for
false positive bird tracks.
Ideally, using only the radar track data is more beneficial
enabling operation without a separate camera sensor. This
would minimize the SWaP of the overall C-UAS system in
addition to the system complexity. Microdoppler is a type of
processing that uses the time varying doppler signature of
target returns to determine if the track is a bird or drone.
Microdoppler [16], [42] is effective but it requires a large
SNR. This is because the returns of rotating blades on a
quadcopter or any UAS with rotating blades are much
Figure 6.
The quadcopter DJI UAS is an example of a low-cost and easily
accessible drone that can be retrofitted and weaponized [39].
40
Figure 7.
The radar array beamwidth is proportional to the array size and
operational frequency. The beamwidth can be reduced by either
increasing the antenna length and/or the operational frequency.
This is depicted by the yellow arrow showing the reduction in
beamwidth to less than 5 is above the contour.
IEEE A&E SYSTEMS MAGAZINE
SEPTEMBER 2023

IEEE - Aerospace and Electronic Systems - September 2023

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - September 2023