A_Study_on_the_Advancement_of_Intelligent_Military.pdf — page 3

M. Lee et al.: Intelligent Military Drone: A Case Study of a Reconnaissance Drone
thetic Aperture Radar (SAR). Notably, there is extensive
research on applying computer vision to drones, actively
merging with other subjects like navigation [16], [17],
[18].
• The field of object detection in computer vision, par-
ticularly the detection of tiny objects from aerial per-
spectives using drones, is an area of active research
[19]. In transportation [20] and agriculture [21], image
segmentation is applied to divide images into multiple
regions, ensuring each region has consistent meaning or
characteristics. This technique is used for distinguishing
roads, soil, and various types of trees.
• Mapping involves creating 2D or 3D maps using drone
sensor data, which include features like terrain, build-
ings, roads, and trees. Navigation is about determining
and following a route to a destination in a given en-
vironment. There are studies on drones simultaneously
performing mapping and navigation in simplified grid
environments, applying reinforcement learning for this
purpose [22]. When considering navigation in a broad
sense to include localization, path planning, and flight
control, it can be said that navigation is the most actively
researched area in the field of drone intelligence, where
deep learning and reinforcement learning are extensively
applied [23], [24], [25].
• Another promising research field related to the intelli-
gence of drones is Multi Agent Systems [26]. Swarm
drones apply multi-agent reinforcement learning for
tasks like target tracking [27], network resource alloca-
tion [28], and weapon-target assignment [29].
• In the field of Communication & Security [30], research
focuses on applying AI technologies to enhance network
systems for drone-to-ground or drone-to-drone data ex-
change. In a more detailed examination of security, there
is research utilizing reinforcement learning to determine
the optimal trajectory for UAVs to avoid jamming [31].
Jamming involves the intentional physical disruption
of wireless signals, leading to the obstruction of com-
munication channels. Furthermore, studies are apply-
ing PCA (Principal Component Analysis), CNN (Con-
volutional Neural Network), and LSTM (Long Short-
Term Memory) in combination to detect GNSS spoofing
[32]. Spoofing is a deceptive practice where signals
are manipulated to mimic or imitate specific devices
or users, misleading the system. Adversarial attacks,
designed to confuse machine learning models, especially
deep learning models, involve adding subtle changes
to model input data, leading to incorrect predictions or
classifications [33]. Research on adversarial attacks and
defenses against deep learning-based drones includes
studies on adversarial patch attacks [34]. These attacks
significantly decrease the image detection accuracy of
drone networks.
• Just as network issues are considered constraints in
drone operations, the field of power and energy manage-
ment also faces challenges. In this area, there are stud-
ies on using reinforcement learning to improve energy
efficiency during drone navigation [35], and research
into automated recharging systems for drones that are
battery-operated and autonomous [36].
• Finally, the embedded systems installed in drones have
performance limitations in terms of computational ca-
pacity, memory, and power consumption. Due to this, the
integration of heavy deep learning models is restricted,
leading to research on lightweight deep learning models
[37].
A. THE CONTRIBUTION OF RESEARCH
Our study’s primary contribution lies in providing intuition
for the intelligence of military drones from an engineering
perspective, based on a high level of understanding in the
military domain. The details are as follows.
• First, we construct a scenario from a military domain
perspective, focusing on a reconnaissance mission car-
ried out by a military intelligent drone. This scenario
describes the process of the drone scouting a specific
sector, detecting and tracking an enemy, and provid-
ing information with high detection reliability to the
decision-maker responsible for attack. Based on this
scenario, we create a custom simulation environment.
• Second, we examine the engineering technologies re-
quired for the operation of military intelligent drones.
These technologies encompass a wide range, from tradi-
tional rule-based and Proportional-Integral-Differential
(PID) control to recent advancements in artificial intel-
ligence. Specifically, we conduct experiments on data
augmentation using Generative Adversarial Network
(GAN) and the training of object detection algorithms,
as well as reinforcement learning for drone movement.
The examination of these engineering technologies ulti-
mately provides insights into how reinforcement learn-
ing can be applied at specific stages to enhance drone
intelligence, presenting solutions to the challenges en-
countered.
III. INTELLIGENT RECONNAISSANCE DRONE CASE STUDY
FIGURE 2. Poongsan corporation’s drone in Unity simulator.
VOLUME 11, 2023
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This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and 
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3390035
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4