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DETECTION AND CHARACTERIZATION OF DRONES IN URBAN ENVIRONMENTS
WITH SYNTHETIC APERTURE RADAR IMAGES
Maryam Abazarsa
1
, Tzuyang Yu
1
,Chiehping Lai
2
, Jack Chuang
2
, and David Griffith
2
1
Department of Civil and Environmental Engineering
University of Massachusetts Lowell
Lowell, MA 01854, USA
2
National Institute of Standards and Technology (NIST),
Gaithersburg, MD 20899, USA
ABSTRACT
This work examines synthetic aperture radar (SAR) imaging
of a commercial drone in an artificial urban environment. We
used a 10 GHz frequency modulated continuous wave
(FMCW) SAR imaging system with a 1.5 GHz bandwidth
inside an electromagnetic anechoic chamber to generate SAR
images for target detection and characterization. We
considered combinations of two ranges, two cross-ranges,
and five elevation angles. We constructed a brick wall inside
the anechoic chamber and behind the drone to simulate the
scenario of a drone flying very close to structures in an urban
environment. From the initial SAR imaging and simulation
results, we found that SAR imaging can detect and
distinguish different drone orientations in an artificial urban
environment.
Index Terms— Drones, SAR imaging, urban
environment, simulation
1. INTRODUCTION
Unmanned airborne vehicles (UAV) and systems (UAS), or
drones, are widely used in various applications in urban
environments, such as for structural inspection [1], traffic
surveillance [2], law enforcement [3], and counterterrorism
[4]. Drones can also be used for intelligence and law
enforcement, i.e., to collect information or jam wireless
communications. Detecting and characterizing drones in
urban environments with different types of background
clutter (e.g., buildings) is essential to monitoring drone
activities effectively.
We constructed a 3-D radar-drone model using
REMCOM’s Wireless Insite software. The tool provides ray-
tracing information between the drone and radar transceiver.
It is used to provide a reference from the theoretical point of
view and has deterministic information, which helps the
development of the SAR imaging algorithm. The model setup
was very close to the laboratory setup. The background
clutter, such as brick walls, glass windows, and other
materials, can be drawn or imported from the third-party
CAD database.
2. RESEARCH APPROACH
2.1 LABORATORY SAR IMAGING FACILITY
We used a laboratory 10 GHz frequency modulated
continuous wave (FMCW) SAR imaging radar system with a
1.5 GHz bandwidth inside an anechoic chamber to generate
all SAR images of a commercial drone. The drone has a 350
mm diagonal length, a 196 mm height, and a 289.5 mm
length/width. We considered two range setups (R = 0.4 m and
1 m) and two cross-range values (CR = 0.4 m and 2 m) in the
laboratory SAR imaging process. We used five tilt angles of
the drone to simulate different orientations of the drone (
,
,
,
, and
). Fig. 1 illustrates two angular drone
orientations in our lab.
Fig. 1. Two angular drone orientations in the lab at UML.
2.2 SIMULATION MODEL
We used the software tool to create the SAR simulation
model, shown in Fig. 2. The CAD model drone is the same
size as the drone used in the measurements. The CAD drone
has 25 000 faces. The SAR simulation model considers one
range set (R = 0.4 m and CR = 2.0 m). There are 15 radar
2024 IEEE Conference on Computational Imaging Using Synthetic Apertures (CISA) | 979-8-3503-0864-8/24/$31.00 ©2024 IEEE | DOI: 10.1109/CISA60639.2024.10576358
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