=Paper=
{{Paper
|id=Vol-3917/paper08
|storemode=property
|title=Channel extractor for UAV PPM signals
|pdfUrl=https://ceur-ws.org/Vol-3917/paper08.pdf
|volume=Vol-3917
|authors=Viktoria M. Smolij,Natan V. Smolij,Oleksii Y. Kovalenko,Mykhailo Z. Shvydenko
|dblpUrl=https://dblp.org/rec/conf/cs-se-sw/SmolijSKS24
}}
==Channel extractor for UAV PPM signals==
https://ceur-ws.org/Vol-3917/paper08.pdf
Viktoria M. Smolij et al. CEUR Workshop Proceedings 226–236
Channel extractor for UAV PPM signals
Viktoria M. Smolij1 , Natan V. Smolij2 , Oleksii Y. Kovalenko1 and Mykhailo Z. Shvydenko1
1
National University of Life and Environmental Sciences of Ukraine, 15 Heroyiv Oborony St., Kyiv, 03041, Ukraine
2
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 37 Beresteiskyi Ave., Kyiv, 03056, Ukraine
Abstract
The problem solved in the work is important for understanding the operation of modern unmanned systems. It
shows how different components interact with each other to achieve the common goal of providing effective UAV
control and real-time video transmission. The obtained results are especially important for solving tasks where
high precision and responsiveness are required, such as reconnaissance missions, environmental monitoring and
rescue operations. The proposed scheme helps improve existing technologies and develop new approaches to UAV
control, making it a valuable tool for engineers and researchers in the field of unmanned aerial vehicles. Studies
of pulse-position modulation were carried out, for which each pulse in the sequence of carrier pulses changes
over time, but without changing the shape and amplitude of the pulse signal. In this paper, a PPM-to-PWM
system is designed. The transformation of PWM signals for several devices, the so-called scaling of the scheme,
was investigated. during simulation, the appearance of the so-called “glitch” after the received pulse of the PWM
signal was detected – a small drop of the pulse over time, which is caused by the reset time of the triggers, and
will not affect the control process.
Keywords
channel extractor, reservoir reconnaissance, signal processing, pulse-position modulation, unmanned aerial
vehicle
1. Introduction and literature review
Nowadays, the design and modeling of drones is an actual, dynamic and extremely complex field of
work, and a lot of research is being conducted in this direction [1, 2]. In work [3] optical camera
communication (OCC) has emerged as a promising alternative technology for radio frequency (RF)-
based communication systems. However, existing OCC approaches only consider transmitting data
through broadcasting, without any ability for point-to-point communication. Deep neural networks
(DNNs) [4] have become a relevant subject in the classification of radio frequency signals and remote
sensing data. A primary challenge is a tradeoff between obtaining data that are suitable for DNN
training and the effort that making experimental measurements requires.
Represents results on the bit error probability (BEP) of Reed Solomon (RS) codes for an optically
pre-amplified pulse position modulation receiver [5]. Analytical relations for the BEP calculation of
the RS coded system were derived and validated via Monte Carlo simulations. The analytical relations
are then utilized to assess the BEP performance of the system in the presence of weak and strong
fading. Khallaf et al. [6] presents accurate approximation expressions for the outage and secrecy outage
probabilities of relay-assisted free-space optical (FSO) communication links utilizing unmanned aerial
vehicles (UAVs).
Nowadays, automatic modulation classification (AMC) has become a key component of next-generation
drone communication systems, which are crucial for improving communication efficiency in non-
CS&SE@SW 2024: 7th Workshop for Young Scientists in Computer Science & Software Engineering, December 27, 2024, Kryvyi
Rih, Ukraine
" vmsmolij@nubip.edu.ua (V. M. Smolij); hoibbitizukrainy@gmail.com (N. V. Smolij); O.Kovalenko@nubip.edu.ua
(O. Y. Kovalenko); shvydenko@nubip.edu.ua (M. Z. Shvydenko)
~ https://docs.google.com/document/d/1iCEX7uqV0ZIMOkTlLUo8FP41EzDd1rQp (V. M. Smolij);
https://www.facebook.com/profile.php?id=61553465353363 (N. V. Smolij);
https://docs.google.com/document/d/10wzZ9w5Cvsxx-HVgc3jgVdzybVkbTXTV (O. Y. Kovalenko);
https://docs.google.com/document/d/1a6v4H7kweLRTp5yhbLTjiZQVW0Cr4qmRfjQkzQTXbD4 (M. Z. Shvydenko)
0000-0002-1268-7837 (V. M. Smolij); 0009-0002-3763-6726 (N. V. Smolij); 0000-0002-9639-3544 (O. Y. Kovalenko);
0000-0002-9025-1326 (M. Z. Shvydenko)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
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Viktoria M. Smolij et al. CEUR Workshop Proceedings 226–236
cooperative environments [7]. Computational imaging breaks the limitation of traditional digital
imaging to acquire the information deeper (e.g., high dynamic range imaging and low light imaging)
and broader (e.g., spectrum, light field, and 3D imaging) [8]. Driven by industry, especially mobile
phone manufacturer medical and automotive, computational imaging has become ubiquitous in our
daily lives and plays a critical role in accelerating the revolution of industry. As various unmanned
autonomous driving technologies such as autonomous vehicles and autonomous driving drones are
being developed, research on FMCW radar, a sensor related to these technologies, is actively being
conducted [9].
The mainstream Global Navigation Satellite System (GNSS) constellations in Medium Earth Orbits
(MEO), Inclined Geo Synchronous Orbits (IGSO) and Geostationary Orbits (GEO), which are primarily
used for Positioning, Navigation and Timing (PNT) can only provide positioning accuracy that varies
from several to dozens of meters, when no Precise Point Positioning (PPP) techniques are used [10].
Lin et al. [11] developed a low-phase-noise, high-sensitivity linear-frequency-modulated continuous-
wave (LFMCW) airborne radar for counter-UAS (unmanned aerial system) applications. Ciesielski et al.
[12] presents research on using multiple signal processors operating simultaneously with different
coherent processing intervals. The papers focuses on considerations related to drone detection. Theo-
retical analysis of the problems arising in FMCW radars is provided and a solution utilizing multiple
signal processors is proposed. Results of field trials are also presented.
Lloyd and Korenberg [13] show improved radar range and velocity resolution is achieved using fast
orthogonal search in place of the standard fast Fourier transform. The method reliably detects targets
that are close either in range or in velocity, which is relevant today given recent advances in target
swarms. WiFi based passive sensing is attracting considerable interest in the scientific community
for both research and commercial purposes [14]. In this work, we aim at taking a step forward in an
endeavor to achieve good sensing capabilities employing compact, low-cost, and stand-alone WiFi
sensors.
Drone-monitoring radars typically integrate many pulses in order to improve signal to noise ratio
and enable high detection performance [15]. Over the course of this coherent processing interval
(CPI), many components of the drone signature change and the signature’s amplitude and Doppler
modulations may hinder coherent integration performance, even in the absence of range-Doppler cell
migrations.
The use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar
and communications communities and several techniques have been explored [16]. In the attempt
of meeting the requirements for small sensor size, compactness, and easy deployment, the authors
consider reference-free approaches, namely approaches that do not require a good copy of the trans-
mitted waveform to be available at the radar receiver. Automatic modulation classification (AMC) is a
signal processing technology used to identify the modulation type of unknown signals without prior
information such as modulation parameters for drone communications [17]. In recent years, deep
learning (DL) has been widely used in AMC methods due to its powerful feature extraction ability.
Safa et al. [18] investigates unsupervised learning of low-dimensional representations from FMCW
radar data, which can be used for multiple downstream tasks in a drone navigation context. To this end,
we release a first-of-its-kind dataset of raw radar ADC data recorded from a radar mounted on a flying
drone in an indoor environment, together with ground truth detection targets. A work by Di Seglio
et al. [19] deals with the short-range monitoring of small radar cross section targets using commercial
WiFi transmitters as source of opportunity. UAVs or drones as an alternative solution to providing
high-quality Internet service in difficult terrain are environmentally friendly and do not consume
electricity during the day as is the case with communication towers [20]. Kim et al. [21] proposed
a drone classification method for polarimetric radar, based on convolutional neural network (CNN)
and image processing methods. We compared the result from the proposed method with conventional
polarimetric radar image structure and achieved similar accuracy while having half of full polarimetric
data.
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2. Theoretical background
The structural diagram of the UAV communication system is shown in figure 1. The structural levels of
the user, the UAV and the direct communication and interaction between them are separately allocated.
The operation of the system begins with the User controls element in the user group, which can be
implemented as a control panel or simulated using a personal computer. This device generates a PPM
signal that contains information about the desired state of the UAV: speed, angular velocities, additional
information from the configuration channels. This signal is coded by the modulator for transmission
over the radio control channel and enters the UAV receiver.
Figure 1: Structural diagram of the UAV communication system.
Further, after decoding the signal, the flight controller receives information from the remote control
and distributes the load on the motor group to meet the user’s requirements. The video from the camera
is used as feedback, which is formed taking into account both the frame from the camera and the
telemetry indicators of the flight controller [22].
Similarly, to the transmission of the signal to the UAV, this frame is forwarded to the user output
system in the form of a display. Drone research is a modern need for technology development. Processing
of data and images by drones to perform the necessary functions requires the construction of certain
structures and algorithms that meet the requirements of speed, reliability and a limited number of
resources.
Controlling drones is complicated by the presence of a large number of obstacles of various origins
and nature. Taking this fact into account when building control systems and data processing algorithms
necessitates the use of signal modulation and, accordingly, the construction of processing models based
on the use of different types of signal modulation. The work is devoted to the study of drones intended
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Viktoria M. Smolij et al. CEUR Workshop Proceedings 226–236
for the study of reservoirs, capable of taking water samples in hard-to-reach places, performing patrols
and carrying out customs supervision.
The task of transmitting an analog signal from potentiometers on a control panel with digital
means is relevant, widespread and important. The parameters of sensitivity, accuracy and speed are
very important here. It should be noted that the analog signal is formed, transmitted and processed
precisely by digital technologies in order to ensure the requirement of speed and improve the clarity
of the processing of the control signal, which in turn can be implemented precisely by pulse-position
modulation (figure 2). Controlling the device with the help of the model illustrated in this article makes
it possible to ensure control quality parameters through certain signal processing and data transmission
(encoding) using the appropriate modulation, which in turn ensures multi-channel control of the device.
Figure 2: Signal decoding [23].
Pulse-Position Modulation (PPM) signals are necessary to control devices that use separate Pulse
Width Modulation (PWM) signals. For example, when there are several devices and you need to connect
them to one data line. In this way, the signals will be separated in time. Without the use of signal
modulation, there may be complications with the transmission of commands and control of the drone.
Modulation allows the transmission of digital commands and data, which simplifies the interaction
between the drone and the operator. The use of signal modulation makes it possible to increase resistance
to noise and other disturbances in the communication channel. Without modulation, there can be
problems with clarity and reliability of communication, which can lead to signal loss or errors in data
transmission. Signal modulation allows the transmission of various types of information, including
control commands, drone status data, instructions for performing various tasks, etc. Without the use
of modulation, the possibility of effective and advanced control may be limited. The lack of signal
modulation can make the communication channel less resistant to attacks and unauthorized access.
Modulation can provide a certain level of confidentiality and security of information transmission.
In general, the use of signal modulation is a key aspect for reliable and efficient communication with
a drone, especially in the unmanned airspace environment, where immunity to interference and control
accuracy are of great importance. When the operator sends a command, a specific time interval is
allocated for each command, and the position of the pulse indicates the specific command or state. This
method allows many different commands to be efficiently transmitted in a single data stream, making it
useful for unmanned aerial vehicles and other remote-control systems.
Nowadays, great variety of electronic modules are controlled with PWM signals, that allow conversion
from digital to analog signals or eases control on duty cycle. Especially it is relevant in terms of drone
technologies almost every part of a drone is controlled with some kind of PWM signals: motors, servos,
analog+ camera interfaces, DC converters. But the main flaw of PWM is that each channel for each
device requires separate wire in order to perform. This problem was solved by using different encodings
of signals. Three main types of encodings are PWM – pulse-width modulation, PPM – pulse-position
modulation and frequency modulation and FM – frequency modulation. Since a lot of devices are
standardized to use PWM signal with length of 20 ms and max duty cycle of 10 percentages it allows
us to pack up to 10 PWM signals in a single period of PPM. But decoding of such signals may cause
some troubles: if there is a line with PPM signal and it is required to separate a single channel from
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this line and give it to specific device available solutions are either to give signal to microcontroller
and program it for purpose of separation a single channel or use ppm decoders that usually channel
specific devices and decode all of channels at the same time and so leads to some pins being unused.
PPM signal example (at the top) and desired circuit outputs per channel (figure 3).
Figure 3: PPM and PWM signal examples [24].
In PWM, the pulse width varies, but the frequency remains constant. Usually, a high pulse width
indicates a large signal, and a low pulse width indicates a small signal. In PPM, the width of the pulses
is fixed, but the moment of their occurrence (position) changes. The width of the pulses may remain
the same, but their position on the time axis changes. Coding is done by changing the pulse width. For
example, a wider pulse may indicate a large or maximum signal, and a narrower pulse – a small or
minimum signal. Coding occurs by changing the position of the pulses. Each pulse represents a separate
signal or channel, and its position on the time axis indicates the value of the signal. Each channel
uses its own PWM signal, and the number of channels is determined by the number of individual
PWM signals. In PPM, the whole signal includes several channels, and each channel is defined by the
position of the pulse on the time axis. The modulator converts the output data into the PPM form of
the signal, determining the time points for the pulses in the transmission interval. If the signal was
modulated during transmission, optional performs demodulation to restore the original PPM signal.
Demodulator determines the value of each pulse relative to time intervals. When processing the received
information, the received data is used to perform appropriate operations or control the drone or other
system. The modulation architecture for the drone includes various components for effective control and
communication. The description of the main ones is given below. The Flight Controller is responsible
for controlling the movement and stabilization of the drone. Its functionality may include navigation
systems, autopilot, stability control, and the like.
The Wireless Communication Module is used to transmit data between the drone and the ground
department or other devices. This may include radio modems, Wi-Fi, Bluetooth or other technologies.
The Signal Processing Module is designed to process signals from various sensors and systems, as
well as to implement stabilization and control algorithms. Antennas and Transceiver Modules provide
uninterrupted communication between the drone and the controller.
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3. The proposed hardware solution for PPM channel extraction
simulation results
The system indicated in the figure is an example of the use of the device in a system with a single PPM
signal line. Model of controlled system is presented in figure 4.
Figure 4: Expected system for designed circuit.
Model of PPM Channel extractor consists of 3 main blocks (figure 5): counter register, user input
channel register, where user can define channel index and compare circuit that changes his state when
required period is reached.
Figure 5: PPM channel extractor system.
3.1. DC component signal conversion
The constant signal component of the DC component in pulse-position modulation is calculated as the
average value of the signal over the period. In general, the formula for the DC component of the PPM
signal looks like this [25]:
∫︁𝑇
1
𝐷𝐶 = 𝑠(𝑡) 𝑑𝑡, (1)
𝑇
0
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where 𝐷𝐶 is the constant component of the signal, 𝑠(𝑡) is the instantaneous value of the PPM signal at
time 𝑡, 𝑇 is the period of the signal.
The PPM signal consists of pulses, the position of which changes depending on the modulation signal.
During the analysis of DC components, both the pulses themselves (their shape) and the duration of the
pauses between them are taken into account. DC component depends on the pulse width and frequency.
If the signal 𝑠(𝑡) is represented by periodic pulses, then the constant component can be simplified to:
𝜏
𝐷𝐶 = 𝐴 · , (2)
𝑇
where 𝐴 is the pulse amplitude, 𝜏 is the pulse width, 𝑇 is the signal repetition period.
The PPM signal is formed by shifting the position of the pulses in time according to the value of the
modulating signal. The general appearance of the signal can be presented as follows:
∑︁
𝑠(𝑡) = 𝑝(𝑡 − 𝑛𝑇 − ∆𝑡𝑛 ), (3)
𝑛
where 𝑠(𝑡) is the PPM signal, 𝑝(𝑡) is the shape of one pulse, 𝑇 is the pulse repetition period, ∆𝑡𝑛 is the
time deviation of the pulse of the 𝑛-th period, which is determined by the modulating signal.
The time shift of the pulse ∆𝑡𝑛 is directly proportional to the instantaneous value of the modulating
signal:
∆𝑡𝑛 = 𝑘 · 𝑚(𝑛𝑇 ), (4)
where 𝑘 is the scaling factor, 𝑚(𝑛𝑇 ) is the value of the modulating signal at the moment 𝑛𝑇 .
The power spectral density of the PPM signal can be calculated taking into account the contributions
of the base pulse and the modulating signal. For a harmonic modulator, the main energy is concentrated
on the harmonics corresponding to the repetition rate 𝑓 = 1/𝑇 .
A signal-to-noise ratio formula that takes into account the effects of sampling and pulse width is
often used to estimate PPM performance in a communication channel.
Signal Power
SNR = (5)
Noise Power
3.2. Schematic primary description
Micro-Cap is used for analog and digital modeling of electrical and electronic circuits with an integrated
visual editor. Allows you to analyze analog, digital and mixed (analog-digital) devices, as well as
synthesize passive and active filters. In atypical situations, it is possible to create your own macromodels
that perform simulation without losing information about the behavior of the system. As it can be seen
in this case there is no need to use extra circuits and extra wires for PPM-PWM conversion and so less
resources are used. Synchro signal is not used commonly in PPM transition but in our case, we need
to synchronize counter registers reset and that can be done either with addition of extra synchro line
or adding extra circuit to module that will compare number of reeded channels with total number of
channels in signal. Example of input signals is presented in figure 6.
In the Micro-Cap system, a circuit based on inverters, XOR, JK flip-flops, XNOR, power cells and
reference voltage sources were modeled. Modelling results is presented in figure 7.
When PPM (blue color) and the RESET signal (red color) are applied to the input of the specified
circuit, after conversion, the output will be a PWM signal from the coded channel (figure 8).
The above diagram shows the conversion of PWM signals for several devices, the so-called scaling of
the scheme (figure 9).
3.3. Results
When applying PPM (blue color) and RESET signal (red color) on the third graph from the bottom
(figure 10), after conversion, the output will be PWM signal for two different channels.
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Figure 6: Circuit inputs (10-channel PPM and synchro signal).
Figure 7: Test circuit.
Figure 8: Modeling result. Green time table at the top – output.
Also, in both cases, we can see the appearance of the so-called “glitch” after the received pulse of the
PWM signal – a small pulse drop over time, which is due to the reset time of the triggers and will not
affect the control process.
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Viktoria M. Smolij et al. CEUR Workshop Proceedings 226–236
Figure 9: System of circuits.
Figure 10: Multi circuit example.
4. Discussion
The task of transmitting an analog signal from potentiometers on a control panel with digital means
is relevant, widespread and important. The parameters of sensitivity, accuracy and speed are very
important here. It should be noted that the analog signal is formed, transmitted and processed precisely
by digital technologies in order to ensure the requirement of speed and improve the clarity of the pro-
cessing of the control signal, which in turn can be implemented precisely by pulse-position modulation.
Controlling the device with the help of the model illustrated in this article makes it possible to ensure
control quality parameters through certain signal processing and data transmission (encoding) using the
appropriate modulation, which in turn ensures multi-channel control of the device. The results of the
work can be used in systems in which it is necessary to control many devices with PWM modulation,
that is, it is necessary to transmit a large number of signals over one communication line.
The obtained results have technical limitations due to the design and conditions of use. The developed
device shows great flexibility and scalability in integration into systems where processing of a large
number of PPM signals is required. This enables its use in a variety of applications and scenarios. The
resistance of the device to various types of interference and noise in the signal transmission environment
was analyzed and confirmed. This makes it reliable and effective in real operating conditions.
The developed device is easily integrated with existing communication systems, which allows it to
be used in large numbers in one system without significant modifications or reconstructions of the
communication system.
Also, ways of further improvement were defined: get rid of synchro signal by adding an extra
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block that will automatically reset the circuit while reaching with counter block pre-defined value that
represents the number of encrypted in PPM channels.
5. Conclusions
The transformation of PWM signals for several devices, the so-called scaling of the scheme, was
investigated. During simulation, the appearance of the so-called “glitch” after the received pulse of the
PWM signal was detected – a small drop of the pulse over time, which is caused by the reset time of
the triggers, and will not affect the control process. The results of the work can be used in systems in
which it is necessary to control many devices with PWM modulation, that is, it is necessary to transmit
a large number of signals over one communication line.
Author Contributions: The idea of writing the article belongs to all authors. Viktoria Smolij built a model, Natan Smolij
conducted the testing and analyzed the results, Oleksii Kovalenko performed the modeling, Mykhailo Shvydenko reviewed
the literature sources.
Acknowledgments: This work was done on initiative. The authors express gratitude for the moral support, scientific
guidance provided during the discussions, and technical assistance in the actual research to Oleksandr Rolik, Yuri Berdnik,
and Mykola Shynkevych.
Declaration on Generative AI: The authors have not employed any Generative AI tools.
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