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1 INTRODUCTION
The development of unmanned aerial vehicles (UAVs)
is one of the most innovative and dynamic areas in the
field of aviation advancement [1]. In recent years, there
has been a significant increase in interest in this topic
from private companies, the military, and
governments around the world [2], [3]. This
phenomenon is driven by the intended use of UAV
systems in medical, transportation, and military
applications. As UAV technology has evolved, so has
the demand for counter-unmanned aerial systems
(C-UASs), called also anti-drone systems [4]. Initially,
UAV technology was used for recreational purposes,
but over time, its range of functions has expanded to
include the execution of a variety of tasks. Application
areas for UAVs (calls also drones) include agriculture,
environmental monitoring, public safety, combat
operations, and the entertainment industry [5], [6]. In
addition, recent years have seen a marked increase in
interest in artificial intelligence (AI) techniques,
enabling the implementation of autonomous systems
in UAV applications [7]. Integrated AI systems allow
for independent decision-making and adaptability to
vicinity changes in the execution of UAV tasks [8].
The combination of sensor arrays, machine learning
algorithms, and modern wireless communication
systems enables task execution with minimal human
involvement [9]. The advancement of UAV
Survey on Intentional Interference Techniques of GNSS
Signals and Radio Links between Unmanned Aerial
Vehicle and Ground Control Station
J. Dułowicz
1,2
, P. Skokowski
2
& J.M. Kelner
2
1
Cyber Defense Forces Component Command, Warsaw, Poland
2
Military University of Technology, Warsaw, Poland
ABSTRACT: With the rapid development of unmanned aerial vehicle (UAV) technology, UAVs are gaining
increasing importance in civilian, industrial, and military applications. UAVs are used for environmental
monitoring, medical deliveries, emergency response, and reconnaissance missions. However, their widespread
use also introduces new security challenges, driving the advancement of counter-unmanned aerial systems
(C-UASs). This paper provides a comprehensive review of intentional interference techniques targeting the radio
links between UAVs and ground control stations, as well as global navigation satellite system (GNSS) signals,
which are essential for autonomous and remote-controlled UAV one operations. The paper examines the radio
frequency spectrum utilized by UAVs and characterizes their typical radio emissions. It presents a detailed
classification of jamming methods, ranging from conventional noise jamming to advanced intelligent jamming
and spoofing techniques. Furthermore, it explores a wide range of spoofing attacks against GNSS receivers,
including replay attacks, signal forgery, estimation-based spoofing, and cooperative advanced methods. Each
technique is analyzed in terms of implementation complexity and operational effectiveness. In addition, the paper
highlights commercial C-UAS solutions, showcasing practical approaches to UAV mitigation through targeted
signal disruption. This review offers an in-depth overview of current threats and defense technologies, serving
as a foundation for future research on adaptive and selective UAV neutralization methods.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 19
Number 3
September 2025
DOI: 10.12716/1001.19.03.27
932
technologies increases their potential for use in
national defense applications [10]. Most commonly,
they are used in reconnaissance operations (such as
border surveillance and intelligence gathering) as well
as in military missions to minimize risk to personnel.
As drones are increasingly used for various purposes,
there is a growing need to develop effective C-UASs
[4]. These systems employ a range of techniques for
UAV detection and neutralization. These include
radar-based systems (both passive and active), visual
recognition techniques, acoustic detection methods
(both passive and active), and radio signal recognition
techniques [11]. In the context of national security, C-
UASs may serve as a component of strategic military
defense, as well as to reduce terrorist attacks or crimes
committed using drones [12].
The rest of the paper is organized as follows. Section
2 presents analysis of the radio spectrum in terms of
UAV navigation and communication systems. Review
of radio emissions used in UAV communication links
and GNSS signals is described in Section 3.
Classifications of jamming methods targeting UAV
radio links and GNSS receivers are shown in Sections 4
and 5, respectively. Section 6 describes sample of
commercial hardware solutions of C-UAS. Finally,
paper summary is contained in Section 7.
2 ANALYSIS OF RADIO SPECTRUM IN TERMS OF
UAV SYSTEMS
In the operation of UAVs the primary control
frequencies are typically defined within the 2.4 GHz
and 5 GHz bands. Additionally, unlicensed bands, so-
called industrial, scientific, medical (ISM) bands,
ranging from 900 MHz to 1.3 GHz, are commonly
utilized for drone communication in scientific,
transportation, and emergency medical response
applications [13]. Considering the capability of a
jamming system to disrupt drones operating across
various frequency bands, it is advisable to implement
detection mechanisms not only for commercial bands
(i.e., 2.4 and 5 GHz) and amateur bands (i.e., 433 MHz),
but also for other ISM bands, in order to enhance the
versatility of the jamming system.
The general range of commercial frequency usage
should be assumed to span from 900 MHz to 5.8 GHz.
However, unlicensed bands above 6 GHz (i.e., from
5.925 GHz to 7.125 GHz) [14], as well as the 433 MHz
amateur band, should not be completely excluded.
UAVs also utilize GNSS technologies to obtain
positional information, support positional correction,
and enable automated route-based flight. GNSSs
commonly used include the United States Global
Positioning System (GPS), China's BeiDou Navigation
Satellite System (BDS), the European Union's Galileo,
and Russia's GNSS (i.e., GLONASS) [15]. These GNSSs
operate within the following frequency ranges [16]:
− GPS: L1 (1227.60 ± 10.23 MHz)
and L2 (1575.42 ± 10.23 MHz);
− GALILEO: E1 (1227.60 ± 1.023 MHz)
and E5b (1575.42 ± 1.023 MHz);
− BDS: B1 (1561.098 ± 2.046 MHz)
and B2 (1207.14 ± 2.046 MHz);
− GLONASS: L1 (1602.5625 ± 4 MHz)
and L2 (1246.4735 ± 4 MHz).
When designing and implementing a potential
C-UAS, it is recommended to develop a jamming or
spoofing solution that targets the most commonly used
communication frequencies between an operator (i.e.,
ground station) and UAV, as well as the frequencies
employed by GNSSs. A more comprehensive solution
should also include other unlicensed and non-standard
(e.g., governmental) frequency bands to ensure
broader system effectiveness.
3 OVERVIEW OF RADIO EMISSIONS USED IN
UAV COMMUNICATIONS AND GNSS SIGNALS
Currently, a significant number of UAVs operate on
frequencies close to those used in the Wi-Fi standard
[17]. This section presents aspects related to the
detection of radio signals emitted by commercial
drones. Radio emissions of an UAV typically consists
of two main components, i.e., control and video
transmission links. The first one is responsible for
motor control and sensor data transmission, while the
second link type is commonly referred to as first person
view (FPV) from the UAV camera. By detecting a
drone’s signal, it is possible to determine the operating
channel by converting the signal from the time domain
into the frequency domain. This is most often
accomplished using the fast Fourier transform (FFT)
algorithm. Figure 1 illustrates a comparison between
the remote-control, Wi-Fi channel, and FPV video
signals in the frequency domain [17].
Figure 1. Comparison of remote-control, Wi-Fi channel, and
FPV video signals.
The remote-control signal is represented by three
narrow sub-bands, each with a bandwidth of up to
2 MHz. The FPV signal is shown as a continuous
wideband signal with a bandwidth of approximately
9 MHz. Both the remote control and FPV signals fall
within the standard 20 MHz Wi-Fi channel. Table 1
presents the frequency ranges for the FPV (map
transmission signal), remote control, and Wi-Fi signals.
Figure 2 depicts spectra of sample radio signals
emitted by a UAV [17].
Table 1. Frequency ranges for the FPV signal, control signal,
and Wi-Fi signal.
Signal type
Bandwidth
Map transmission (FPV)
signal
9 MHz
Remote-control signal
2 MHz
Wi-Fi signal
20 MHz
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a)
b)
Figure 2. Sample spectra of radio signals emitted by UAV: (a)
with and (b) without of FPV signal [17].
Figure 2 (a) shows that the spectrum of radio
signals emitted by a UAV can be divided into several
key segments. The initial segment constitutes the first
part of the remote-control signal with a bandwidth of
approximately 2 MHz. Next, there is an FPV sub-band
with a bandwidth of about 9 MHz. The last two sub-
bands correspond to the second and third parts of the
remote control signal, each with a bandwidth of
approximately 2 MHz. It should be noted that the
remote-control signal is characterized by a higher
power level and significantly narrower bandwidth
compared to the FPV signal, which may be important
when selectively jamming specific portions of the UAV
radio transmission. In the case of jamming only the
remote-control signal, it is possible to deceive the UAV
operator by making the signal quality degradation
appear unintentional. Figure 2 (b) presents the UAV
emission excluding the FPV signal.
As we mentioned, each UAV additionally is
supported by GNSS. Figure 3 draws exemplary spectra
of different GNSS signals [18].
Figure 3. Sample spectra of GNSS signals [18].
When attempting for jamming the connection with
a UAV or spoofing GNSS signals, it should be
considered the operating frequency ranges of these
systems, the power levels of the signals, as well as the
their channel bandwidths.
4 CLASSIFICATION OF UAV RADIO LINK
INTERFERENCE AND JAMMING METHODS
This section describes a classification of jamming
methods targeting UAV radio links. Jamming is
defined as intentional interference caused by the
generation of radio signals with sufficient power to
disrupt the operation of the targeted device. Often,
when the useful signal reaches the receiver along with
a jamming signal, it becomes impossible to extract
meaningful information [19]. Jamming methods can be
classified as follows [20]:
− narrowband noise jamming [21], [22], [23];
− wideband noise jamming (“brute-force”) [21], [24],
[25];
− ultra-wideband (UWB) noise jamming [15], [26];
− swept noise jamming [20], [27], [28];
− intelligent noise jamming [20] [24] [29];
− response jamming (spoofing) [30], [31].
4.1 Narrowband Jamming
Narrowband jamming can be classified as single-
frequency jamming, pulsed jamming, or low-
bandwidth signal jamming [22]. Unlike wideband
jamming, narrowband jamming can achieve higher
instantaneous power values, which also affects its
effective range [21], [23].
4.2 Wideband Noise Jamming
Noise jamming is a method of interference using noise
with specific characteristics, power level, and
bandwidth, aimed at reducing the signal-to-noise ratio
of the useful signal. This type of interference is widely
used in communication channel disruption [21], [24].
In this type of jamming, studies have been conducted
on the effectiveness of Gaussian noise and phase noise,
tested through simulation methods [25].
4.3 Ultra-Wideband Jamming
UWB jamming uses a wideband noise signal that spans
the entire frequency range of the targeted signal. As a
result, this type of jamming can be used to disrupt a
UAV control channel, video link, or the entire UAV
communication link. However, its major drawback is
the high-power requirement needed to ensure a
sufficient noise-to-signal ratio to effectively jam the
selected signal [15], [26]. Figure 4 presents a
comparison of spectra of narrowband (a) and
wideband (b) jamming signals.
4.4 Sweep Noise Jamming
Sweep noise jamming is a type of jamming that
involves rapidly sweeping a relatively narrowband
signal across the entire frequency range of the target
signal [28]. The jamming signal may consist of noise or
pulsed signals. At any given moment, only a specific
frequency band is jammed, which may be insufficient
for disrupting systems that use frequency-hopping
spread spectrum (FHSS). Although the frequency
range of the jamming signal overlaps with that of the
target signal, there is no guarantee that the ranges will
align at every moment during the jamming process.
However, this form of jamming is effective against
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direct sequence spread spectrum (DSSS) signals with
stationary spectra [20]. Figure 5 shows an example of
fifth-generation (5G) channel jamming using the sweep
noise jamming method, with a sweep rate of 10 hops
per second [27].
(a)
b)
Figure 4. Comparison in frequency domain between (a)
narrowband and (b) wideband jamming for example signal
[20].
Figure 5. Example of 5G channel jamming using sweep noise
technique with sweep rate of 10 changes per second [27].
4.5 Intelligent Noise Jamming
Intelligent noise jamming uses only those signals that
are necessary to disrupt communication between the
transmitter and the receiver of the desired signal. Prior
knowledge of the type of signal being jammed is
required in order to determine its structure in relation
to relevant frequency sub-bands. Based on the analysis
of selected UAV communication protocols and the
received signal, it is possible to identify critical points
where interference can disrupt the connectivity of the
given system. This method allows for energy-efficient
and less invasive generation of a jamming signal by
reducing the bandwidth of the jamming spectrum. The
method of generating such a signal is similar to
response jamming or spoofing [20], [24], [29].
4.6 Response Jamming (Spoofing)
Response jamming, also known as spoofing, is a
method of generating falsified signals based on the
received signal. Unlike traditional jamming methods,
this technique does not aim to interrupt
communication between the transmitter and receiver
but instead it generates an artificial signal intended to
deceive the receiver (in this case, a UAV). This involves
receiving the original signal, analysing and extracting
the necessary data, modifying it, and then generating a
fake signal in such a way that the receiver is unaware
that it comes from an external source. The growing
number of UAV manufacturers and communication
protocols makes it difficult to develop a universal
spoofing approach. However, some existing UAVs
have been subjected to practical tests that determine
their vulnerability to spoofing attacks [30], [31].
5 CLASSIFICATION OF GNSS JAMMING AND
SPOOFING METHODS
For most GNSS receivers, the received satellite signal
strength is very low, making it susceptible to
interference. To minimize the impact of interference on
GNSS reception, filtering systems are used to reduce
noise in the receiver chain. However, when a jammer
or spoofer is used, there is a high probability that the
real GNSS signal will be filtered out and the receiver
will instead accept the interference or the spoofed
signal [32]. GNSS spoofing attacks can be classified as
follows:
− Replay spoofing attack (RSA):
− Direct replay type interference [33],
− High power replay interference [34], [35],
− Selective delay replay interference [36], [37],
[38], [39],
− Multi-antenna receiver replays interference [40];
− Forgery spoofing attack (FSA):
− Direct-generation forgery [41],
− Analysis-generation forgery [42], [43],
− Denial environment forgery [44],
− Full-channel forgery [45], [46];
− Estimation spoofing attack (ESA):
− Security code estimation and replay [47],
− Forward estimation attack [48];
− Advanced spoofing attack (ASA):
− Nulling attack [40],
− Cooperative interference attack [49].
5.1 Replay Spoofing Attack
Replay spoofing involves delaying the transmission of
a GNSS signal received by the receiver using a spoofing
system. These types of attacks are simple to implement
and offer moderate effectiveness.
5.1.1 Direct Replay Type Interference
This type of interference involves recording and
replaying a received GNSS signal. It is generally not
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used due to its low effectiveness compared to other
methods [33].
5.1.2 High Power Replay Interference
This method uses a high-power spoofed signal to
deceive the GNSS receiver. The receiver may interpret
the spoofed signal as genuine, mistaking the real
satellite signal for multipath interference, thereby
accepting the fake signal [34], [35].
5.1.3 Selective Delay Replay Interference
This interference method aims to delay the satellite
signal in such a way that it alters the phase of the
spreading code reaching the GNSS receiver [36], [37],
[38], [39].
5.1.4 Multi-Antenna Receiver Replay Interference
In this method, multiple jamming devices located at
different positions are used, making it harder for a
GNSS receiver with a multi-antenna system to detect
the angle of arrival of the spoofed signal [40].
5.2 Forgery Spoofing Attack
Forgery spoofing adjusts the receiver’s signal
parameters and generates a fake GNSS signal that leads
to erroneous position readings. This method is of
moderate implementation complexity and moderately
good effectiveness.
5.2.1 Direct-Generation Forgery
This spoofing technique uses an field-
programmable gate array (FPGA), digital signal
processor (DSP), or software-defined radio (SDR) to
directly emulate GNSS signals based on preloaded
parameters. A radio signal is generated that closely
mimics a real satellite signal. Some GNSS receivers
may reject the spoofed signal. This method requires
only a transmitter (no receiver) [41].
5.2.2 Analysis-Generation Forgery
Similar to direct-generation forgery but includes a
receiver and analytical modules. Based on the real
GNSS signal, the system creates a spoofed signal with
parameters similar to the actual ones at a given time
and location [42], [43].
5.2.3 Denial Environment Forgery
An advanced form of spoofing that builds on
analysis-generation forgery by first emitting strong
interference to block satellite signals. After the
disruption, a spoofed GNSS signal is generated and the
interference is stopped, increasing the chance that the
receiver will accept the fake signal as genuine [44].
5.2.4 Full-Channel Forgery
Full-channel forgery is an advanced spoofing
technique that emulates fabricated GNSS signals across
all satellite channels simultaneously [46]. This method
is significantly more convincing than previous
techniques and remains effective even when basic anti-
spoofing measures are in place. As an additional
classification, spoofing attacks can be divided into
static and dynamic types [45]. In the case of the static
attack, a spoofed GNSS signal is sent to the UAV that
does not change over time. Despite the drone’s actual
physical movement, the GNSS system will indicate that
the UAV is fixed. This type of attack may be useful for
simulating system failures or feeding false status
information to the UAV’s control system (i.e.,
operator). Figure 6 illustrates an example of a drone
being spoofed using a static attack.
Figure 6. Spoofing UAV position using static attack.
Figure 7. Spoofing UAV position using dynamic attack.
A dynamic spoofing attack involves delivering an
emulated GNSS signal to the detected UAV, containing
variable signals corresponding to different positions (a
multi-point set of coordinates that change over time).
In this case, the GNSS system will indicate that the
UAV is moving along a predefined path, regardless of
its actual position or whether the drone is physically in
motion. Figure 7 presents a UAV flight path map
resulting from a dynamic spoofing attack. In this case,
the blue line indicates the initial and final position of
the GNSS receiver, while the green line indicates the
initial and final spoofed location generated by the
spoofer.
5.3 Estimation Spoofing Attack
Estimation spoofing targets certain civilian satellite
signals by estimating unknown security codes. The
method predicts satellite information and generates
spoofed GNSS signals based on the estimated values
intended for the GNSS receiver.
5.3.1 Security Code Estimation and Replay
Security code estimation and replay is a method
involving the emulation of satellite signals using an
artificially generated encryption algorithm. In practice,
this method is not commonly used due to its high
implementation complexity [47].
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5.3.2 Forward Estimation Attack
Forward estimation attack targets the portion of
GNSS messages that contains authorization data. It is
rarely used in practice because of the difficulty of
implementation, similarly to security code estimation
and replay technique [48].
5.4 Advanced Spoofing Attack
Advanced spoofing targets more sophisticated GNSS
receivers equipped with spoofing prevention
mechanisms. This method generates spoofed signals
based on combinations of characteristics of potentially
received signals for more effective deception. It often
involves multiple spoofing modules working together.
5.4.1 Nulling Attack
Nulling attack is an advanced technique that
involves simultaneously transmitting a copy of the
signal expected to be received by the GNSS receiver
from multiple transmitting units. These transmitted
copies have an inverted signal phase, which, when
reaching the receiver, causes destructive interference
with the satellite signal, effectively preventing
reception. This method is known for its exceptional
effectiveness [40].
5.4.2 Cooperative Interference Attack
Cooperative interference attack is a complex
combination of multiple jamming techniques using
several coordinated jamming systems. Even when
sophisticated anti-spoofing techniques are employed,
this method can prevent the end user from receiving
valid data or ensuring data integrity [49].
5.5 Summary of GNSS Spoofing Techniques
Table 2 presents a summary of all discussed jamming
and spoofing methods based on their implementation
difficulty and operational effectiveness [32].
6 EXAMPLES OF COMMERCIAL HARDWARE
SOLUTIONS FOR JAMMING UAV RADIO LINKS
AND SPOOFING GNSS RECEIVERS
With the growing use of UAVs in both commercial and
recreational applications, there is an increasing need to
ensure security and privacy. As a result, there is a
rising demand for technological solutions that can
control and restrict UAV operations in designated
areas. One such solution involves commercial systems
designed to jam GNSS signals and UAV
communication links.
In this section, we present three examples of
C-UASs with significantly focusing on radio frequency
(RF) sensors and effectors. Broader overviews of
C-UASs, including other types of sensors and effectors,
are included in [4], [12].
6.1 DRONESHIELD DroneGun Tactical
The DRONESHIELD DroneGun Tactical [50] is a non-
kinetic C-UAS that uses jamming emitters. It is a
portable, rifle-shaped device equipped with wide-
range directional antennas and a control panel that
allows the operator to select the frequencies to be
jammed. By emitting targeted interference, the system
forces the UAV to either land at a designated location
or return to its take-off point. Figure 8 shows the
DRONESHIELD system [50].
Figure 8. Handheld DRONESHIELD DroneGun Tactical
system [50].
Table 2. Advantages and limitations of GNSS spoofing methods [32].
Type of spoofing attack
Implementation difficulty
Effectiveness
Replay spoofing attack (RSA):
Low
Moderate
Direct replay interference
Low
Low
High power replay interference
Low-medium
Moderate
Selective delay replay interference
Low-medium
Moderate
Multi-antenna receiver replay interference
High
Moderate-good
Forgery spoofing attack (FSA):
Medium
Moderate-good
Direct-generation forgery
Medium
Moderate
Analysis-eneration forgery
Medium-high
Moderate-good
Denial environment forgery
Medium
Moderate
Full-channel forgery
High
Good
Estimation spoofing attack (ESA)
Medium-high
Good
Forwarding spoofing attack
Medium-high
Good
Generation spoofing attack
Medium-high
Good
Advanced spoofing attack (ASA):
High
Good
Nulling attack
High
Moderate-good
Cooperative inference attack
High
Good
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6.2 Anti-UAV Defence System (AUDS)
Figure 9. AUDS system in stationary configuration [51].
Anti-UAV Defence System (AUDS) [51] is a C-UAS
developed by British companies Blighter Surveillance
Systems, Chess Dynamics, and Enterprise Control
Systems. It is intended for use by the military,
governments, and homeland security forces to detect,
track, classify, and disrupt threats posed by micro,
mini, and large UAVs. The system features a
directional radio frequency jammer used to disrupt the
UAV’s control signal. It employs four high-gain
antennas and covers the frequency bands of the GNSS.
AUDS can be deployed in various terrains and can
operate from fixed installations or mobile platforms.
Figure 9 depicts the AUDS in its stationary
configuration.
6.3 DRONE DOME
The DRONE DOME system [52] provides a complete
and comprehensive solution aimed at restricting
unauthorized UAV flights within designated zones.
DRONE DOME is capable of identifying unknown
targets, generating alerts (based on a customizable rule
engine), and operating without interfering with non-
targeted aircraft by utilizing a defined jamming
bandwidth and an advanced directional antenna. This
feature makes DRONE DOME particularly effective in
highly congested airspaces (civilian or military).
DRONE DOME is a modular C-UAS that can be
deployed in stationary, mobile, or customized
configurations. Figure 10 presents the DRONE DOME
system.
Figure 10. DRONE DOME system [52].
7 SUMMARY
The rapid development and growing ubiquity of UAV
have necessitated the advancement of
countermeasures capable of neutralizing potential
threats posed by unauthorized or hostile drone
operations. This survey presented a comprehensive
overview of intentional interference techniques
targeting UAV communication links and GNSSs,
which are critical for drone control and positioning.
Key aspects of the radio spectrum used by UAVs
were analyzed, emphasizing the dominant use of
unlicensed ISM bands and the dependence on multiple
GNSS constellations. A detailed examination of radio
emissions revealed the typical structure and
bandwidth characteristics of UAV control and video
transmission links, underscoring their susceptibility to
various forms of jamming. The paper classified
multiple radio jamming methods, ranging from basic
noise jamming to more advanced intelligent and
response-based spoofing techniques. Similarly, GNSS
spoofing was categorized into replay, forgery,
estimation, and advanced attack strategies, each
evaluated in terms of implementation difficulty and
operational effectiveness. The final section presented
real-world C-UAS solutions. These examples
demonstrate how commercially available technologies
can detect, track, and disable UAVs by targeting their
radio and navigation links without physical
destruction.
Overall, the paper highlights that as UAVs continue
to evolve, so must the techniques and technologies
used to counteract them. Future efforts should focus on
the development of adaptive, intelligent, and
minimally invasive countermeasures capable of
responding dynamically to emerging UAV
technologies and increasingly sophisticated
communication protocols.
ABBREVIATIONS
5G – fifth-generation
AI – artificial intelligence
ASA – advanced spoofing attack
AUDS – Anti-UAV Defence System
BDS – BeiDou Navigation Satellite System
C-UAS – counter-unmanned aerial system
DSP – digital signal processor
DSSS – direct sequence spread spectrum
ESA – estimation spoofing attack
FFT – fast Fourier transform
FHSS – frequency-hopping spread spectrum
FPGA – field-programmable gate array
FPV – first person view
FSA – forgery spoofing attack
GNSS – global navigation satellite system
GPS – Global Positioning System
RF – radio frequency
RSA – replay spoofing attack
SDR – software-defined radio
UAV – unmanned aerial vehicle
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UWB – ultra-wideband
ACKNOWLEDGEMENTS
This work was developed within the framework of the
research grant no. UGB/22-059/2025/WAT, sponsored by the
Military University of Technology (WAT), Poland.
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