IEEE - Aerospace and Electronic Systems - May 2022 - 15

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Figure 1 shows the different technologies being considered
for UAS detection, classification, and mitigation.
The various sensors that have been researched for
Figure 1.
UAS detection, classification, and mitigation technologies.
detection and classification all come with different benefits
and their own challenges. Radar, for example, boasts
long ranges and has advantages over imagery-based methods
as it is resilient to weather conditions. Acoustic sensors
have proved very accurate in short range and like
radar performance they do not degrade with visibility but
acoustics systems do suffer when noise is present, including
wind. Imagery-based systems using electro-optical
cameras are generally low in cost and human verification
is possible but detection and classification accuracy is
affected by environmental conditions and time of day.
Laser- and thermal-based systems can be costly and thermal
has shown to be less effective with power efficient
UASs, such as some quadcopters. Figure 2 shows an
example of a small quadcopter in flight.
Radio frequency (RF) systems can produce ranges simFigure
2.
Small UAS in flight.
MAY 2022
ilar to radar, they do not suffer from environmental conditions
and classification can specify what the UAS is
doing-i.e., stationary, hovering, or flying. It is worth noting
that up until 2019 when classification was referred to
with UASs it considered issues, such as distinguishing
between UAS and birds, and classifying the UAS type.
Figure 3 shows an RF detection and classification system
for small UAVs. Al-Sa'd et al. [13] and Swinney and
Woods [14]-[16] considered a new type of classification
by classifying the flight mode. As we come to review recent
security incidents in this article, we can keep in mind that
understanding what a UAS is doing, for example, capturing
video or hovering over a particular site, could impart valuable
information contributing to an assessment of risk.
Classifying the flight mode is an important step forward
and a significant benefit of RF detection and classification
systems. However, these systems cannot
detect UASs flying without active RF datalinks, such
as UASs flying in a GPS autonomous mode. RF detection
when combined with machine learning can
increase both reliability and system performance [17].
IEEE A&E SYSTEMS MAGAZINE
15

IEEE - Aerospace and Electronic Systems - May 2022

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - May 2022

Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Cover1
IEEE - Aerospace and Electronic Systems - May 2022 - Cover2
IEEE - Aerospace and Electronic Systems - May 2022 - Contents
IEEE - Aerospace and Electronic Systems - May 2022 - 2
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