=Paper=
{{Paper
|id=Vol-3732/paper15
|storemode=property
|title=Software detection and denying false GNSS data on open-source UAV autopilot
|pdfUrl=https://ceur-ws.org/Vol-3732/paper15.pdf
|volume=Vol-3732
|authors=Bohdan Blazhei,Vitalii Larin,Nataliia Kuzmenko
|dblpUrl=https://dblp.org/rec/conf/cmse/BlazheiLK24
}}
==Software detection and denying false GNSS data on open-source UAV autopilot==
https://ceur-ws.org/Vol-3732/paper15.pdf
Software detection and denying false GNSS data on open-
source UAV autopilot
Bohdan Blazhei 1,†, Vitalii Larin1,†and Nataliia Kuzmenko1,†
1 National Aviation University, Liubomyra Huzara Ave., 1, Kyiv, 03058, Ukraine
Abstract
Performing safe flights of unmanned aerial vehicles (UAV) in busy areas is crucial to ensure their
seamless integration into our modern-day tasks. The most popular UAV autopilot solutions show that
safety could be easily tampered with by intended purposes or by interference caused by surrounding
equipment. Modifications of the extended Kalman filter filter, which most autopilots use for
navigation solution estimation, have been introduced in the paper to minimize the effect of
interference. Results of the study present flight data collected and processed by common UAV
autopilot unit during normal operation.
Keywords
air navigation, GNSS, extended Kalman filter, UAV, ardupilot, INS, FMU
1. Introduction
The technology of unmanned aviation is an excellent example of modern engineering where the
interaction of the drone's design, its software-controlled electronic equipment, and information
technology is combined. Only the combined interaction of these components makes both the
flight of the unmanned aerial vehicle (UAV) and the maintenance of it and its ground station
possible. The vast majority of professional and regular users, engineers, and UAV pilots are
familiar with the flight planning software Mission Planner. Information technologies are used to
solve tasks related to designing components of unmanned aviation systems, simulation of the
flight of UAV, and simulating the characteristics of its components. The widespread use of
software allows for the development of algorithms for upgrading and improving various aspects
related to unmanned aviation systems. One of the most popular solutions for onboard software
is Ardupilot and PX4, which are open-source flight control software that runs on standardized
hardware of flight management unit.
CMSE’24: International Workshop on Computational Methods in Systems Engineering, June 17, 2024, Kyiv, Ukraine
∗ Corresponding author.
† These authors contributed equally.
4538392@stud.nau.edu.ua (B. Blazhei); vjlarin@gmail.com (V. Larin); nataliiakuzmenko@ukr.net (N. Kuzmenko)
0009-0005-3616-4239 (B. Blazhei); 0000-0002-5042-2426 (V. Larin); 0000-0002-1482-601X (N. Kuzmenko)
© 2024 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
2. Related works
The main intellectual component of UAV is autopilot, which combines the aircraft control system
and the navigation system. The navigation component of a UAV can be conditionally divided into
two parts: one that depends on external signals and one that is autonomous. The set of
navigation equipment on board a UAV is currently not clearly defined due to existing technical
and organizational-regulatory issues. In the field of civil UAV applications, researchers pay
considerable attention to aspects of ensuring UAV flight safety, which is the primary task of civil
aviation. Flight safety depends on a significant number of factors. For example, an onboard
weather radar is not standard equipment for UAVs, but its absence can, under certain
circumstances, increase flight safety risks. The presence of such a component enhances the level
of situational awareness about various meteorological phenomena. The algorithm presented in
[1] will allow identifying the degree of turbulence intensity.
Much attention is paid to the search for new navigation solutions. For instance, navigation
tasks are proposed to be solved through relative navigation of moving objects by correcting
navigation decisions from mobile communication station signals [2]. Researchers also focus
significantly on independent navigation systems. In [3], a local orientation system containing an
inertial measurement unit and a magnetometric sensor is proposed, which algorithmically
accounts for interference signals from powerful electromagnetic radiation sources. The
improvement of navigation tools themselves is also being investigated.
Paper [4] proposes the structure of an inductive magnetometric sensor with non-orthogonal
sensitivity axes, which is used to determine the course of the aircraft. The flight duration of
electrically powered UAVs is influenced by the capacity of the battery.
The authors of [5] propose an algorithm for the onboard battery management system based
on developed fuzzy logic rules, which allows predicting the impact of external factors such as
temperature, humidity, and crosswind effects on the battery discharge rate. Algorithms for
computing navigation data are also a relevant area of research.
In the articles [2, 6], an economic method, in terms of computational power, is developed to
minimize the errors of trajectory measurements obtained from an automatic dependent
surveillance device or a barometric altimeter. The issue of operational reliability is also an
important research task.
Researchers [7] propose a hierarchical monitoring system built to support the life cycle of
aviation equipment. The use of the regression model proposed by the authors [8] will improve
the accuracy of assessing the degree of degradation of aviation tools.
The authors [9] examine a 15-state extended Kalman filter (EKF) and a hybrid architecture
combining a six-state nonlinear complementary filter (NCF) with a nine-state EKF. They integrate
GPS data with inertial measurement units, which include three-axis accelerometers, three-axis
rate gyros, and a three-axis magnetometer, in an open-loop configuration to estimate
navigation states. These architectures were assessed in the closed-loop guidance of the Black-
Kite MAV using a software-in-the-loop simulation (SILS) setup. Both algorithms are validated
using flight test data recorded by an on-board data logger on an off-the-shelf autopilot board
(Ardupilot Mega APM-2.5) mounted on the “Slybird” UAV [10, 11]. The proposed architectures
are crucial for achieving fusion of inertial navigation system and global navigation satellite
system (GNSS) sensors, which is essential for the autonomous guidance and navigation of UAVs.
The paper presents two INS/GNSS fusion/filter algorithms for Black-Kite MAV, evaluating their
performance in the SILS setup. The filters are evaluated with position, velocity, attitude, and
heading estimates from both fusion architectures. The performance of both filters is compared
with the flight test data of SLYBIRDUAV, obtained from the autopilot board ARDUPILOT MEGA
(APM-2.5) with onboard MEMS sensors and data logger. The paper presents two INS/GNSS
mathematical model formulations: first, 15-state EKF, and second, NCF-EKF split architecture
with six-state NCF and nine-state EKF [12]. The MEMS sensor suit for signal measurements
consists of tri-axial accelerometers, tri-axial gyroscopes, tri-axial magnetometer, and GNSS. The
constant bias components are constant null-shift bias terms, while the accelerometer
measurements are modeled as zero mean, band-limited AWGN processes with covariances. The
15-state EKFN model excludes the effect of Earth's rotation rate, and the INS/GNSS model in the
local North-East-Down frame is used. The six-state NCF is used for estimating attitudes and rates
bias, while the nine-state EKF is used for estimating position and velocity. This approach allows
for practical realization of estimation problems, such as providing decent attitude solutions
during GNSS outages. Spoofing is the practice of replicating false signals with the same code
phase, carrier frequency, and Doppler frequency shift as the real navigation satellite signal to
achieve interference and capture [13].
After introduction of GNSS spoofing technology, the research progress of GNSS anti-spoofing
technology over the last decade is summarized. A new classification standard is proposed for
anti-spoofing technology and the implementation difficulty, effect, and adaptability of the
current main spoofing detection technologies are analyzed and compared. Spoofing is the
practice of replicating false signals with the same code phase, carrier frequency, and Doppler
frequency shift as the real navigation satellite signal to achieve interference and capture. It has
become a hotspot for satellite navigation interference technology due to its advantages in
interference concealment and efficiency. Spoofing of GNSS involves broadcasting false signals
to make the victim receiver misunderstand them as real signals, leading to incorrect positioning
and timing, potentially causing dangerous behavior. GNSS anti-spoofing technology aims to
detect attacks and warn victims that their navigation and clock are unreliable. Onboard receivers
with receiver autonomous integration monitoring technology (RAIM) use redundant signals by
default to generate multiple positions for comparison. However, some spoofing methods may
exceed USE's basic defense capability. This paper introduces the development of satellite
navigation spoofing technology, focusing on GNSS positioning principles, vulnerabilities,
spoofing attack methods, and defense methods. GNSS vulnerability is based on three main
factors: disclosure of navigation signal format, disclosure of navigation data format, and an
unprotected broadcast channel. GNSS uses three public frequencies which expose the spectrum
characteristics, signal modulation format, and pseudo-random code sequence. This allows
spoofers to take targeted spoofing actions based on relevant signal parameters and
characteristics. GNSS message data, such as ephemeris, almanac, satellite clock parameters, and
ionosphere/troposphere, are disclosed to facilitate user use. GNSS's broadcast communication
mode exposes its communication channel to interference, monitoring, and tampering. GNSS
spoofing involves transmitting a signal with the same structure and power as the satellite signal,
causing the target to mistakenly think it is a real signal and search for and capture it. Spoofing
affects satellite navigation signal processing, which includes RF front-end processing, baseband
IF signal processing, and navigation information output [10].
The researchers [14, 15] propose a Kalman Filter design to detect spoofing using residual
analysis and provide countermeasures. The performance of the filter is tested using Monte-
Carlo simulations. The results show that the proposed filter is successful in detecting spoofing
attacks and correcting position and velocity estimations, reducing vulnerability against spoofing
and increasing the robustness of the navigation solution. The Kalman Filter design is proposed
for detecting spoofing attacks in GNSS receivers. The filter structure includes initial states, error
covariance matrix, and state estimations. The filter operates at a frequency of 5 Hz, with GNSS
measurements coming every 1 second. The residual generation is added to the filter to detect
anomalies. When an anomaly is detected for 10 time steps, it is considered a spoofing attack.
Dynamic calibration and compensation method is proposed in [16] for errors caused by time-
gap between two asynchronous INS in carriers. The method analyzes and models the causes of
asynchronous time from different INSs, establishing an online estimation and compensation
Kalman filter for asynchronous time. Simulation results show the proposed method can achieve
an estimation accuracy of 0.027ms for the asynchronous time between different INSs, improving
the accuracy and stability of the fusion algorithm. A framework for GNSS spoofing detection
combines multiple metrics with a fixed false alert probability, achieving over 70% reduction in
worst-case missed detection probability compared to conventional metric combination
techniques. This is particularly important for real-time applications [17]. Based on the traditional
principle of using a multi-antenna carrier phase to solve DOA, this paper [18] innovatively solves
the following problems: the poor direction-finding accuracy caused by the unstable phase center
of low-cost commercial antennas, the low success rate of spoofing detection in a multipath
environment, and the inconsistent sampling time among multiple low-cost commercial GNSS
boards. Monte Carlo simulations are carried out to verify our analysis, which shows the
effectiveness of the Kalman filter innovation-based spoofing detection method against ramp-
type fault profiles and the advantages of measurement averaging over innovation averaging in
certain spoofing scenarios [19]. In the GNSS/RINS integrated navigation system, the results of
RINS are free from external interference and have sufficient accuracy in a short time [20].
The paper [21] analyzes the detection performance of a signal quality monitoring (SQM)
method for detecting GNSS spoofing, focusing on the fusion of metrics using an “OR” rule and
determining optimal thresholds and detection probability. To effectively combat intermediate
spoofing signals, this paper presents an enhanced spoofing detection method based on
abnormal energy of the quadrature (Q) channel correlators [22]. The extended Kalman filter
estimated position and velocity of the receiver is used along with the satellite position and
velocity computed from ephemeris to find out the range and range rate of each of the satellites
to the receiver [23].
The test [24] is based on the generalized likelihood ratio test (GLRT) paradigm and essentially
performs a consistency check between the set of observed range measurements and known
information about the satellite topology and the geometry of the receiver constellation. This
paper [25] presents a framework for GNSS spoofing detection using the Generalized Likelihood
Ratio Test, demonstrating robustness against various attack modes and ensuring false alert
probability under the Neyman-Pearson paradigm.
3. Problem statement
A receiver of GNSS is an integral component of most UAV navigation systems. Today, several
GNSS are actively functioning, with dozens of satellites in Earth's orbits providing signals to users
and enabling highly accurate determination of object coordinates.
One of the weaknesses of satellite navigation is the vulnerability of the signals received by
the onboard navigation system receiver to external interference of natural and artificial origin.
An especially dangerous factor affecting UAV navigation systems is deliberate interference,
which involves substituting the true satellite system signals with an external “satellite-like” false
signal, usually of higher power. This allows the owner of such a signal, by generating the required
sequences, to direct the UAV's flight according to their own scenario. This type of interference
is known as "spoofing". Spoofing is more subtle than jamming and relies on generating a
counterfeit signal with just the right strength to “lift” a timer or navigation receiver from the
legitimate signal [14]. The ultimate goal of such spoofing influence is either an aviation disaster
of the affected UAV or diverting the UAV significantly away from the flight path designated by
preprogrammed waypoints.
One of the most common open-source software environments for UAV flight control today
is Ardupilot, which can interact with a wide range of hardware systems. When performing UAV
flights using Ardupilot as an autopilot in complex radio conditions with standard onboard GNSS
receivers, a threatening situation may arise during the flight. When the UAV enters a zone with
satellite navigation signal suppression, the GNSS receiver data transmitted to the flight
controller is incorrect, specifically exhibiting a jumpy, prolonged change in the current
coordinates and flight altitude reported by the receiver. This problem is quite relevant as it
significantly and directly affects the safety of UAV flights.
4. Data acquired during GNSS jamming and spoofing
In order to proceed with practical data gathering a test environment was setup. An electronic
warfare system Bukovel-AD was used [26]. Test airframe type is VTOL (vertical take-off and
landing) airplane. Flight plan included takeoff on VTOL to height of 50m, climb to 900m AGL
(above ground level), performing flight in range of 10 km from Bukovel-AD.
During the test flight the following equipment have been used: HEX Cube Orange+
(autopilot); ArduPlane V 4.4.1 (firmware), and Ublox F9P (GNSS receiver). Trajectory data for
test flight are presented in Figure 1 and Figure 2.
Climbing mode is indicated from 22:21 until 22:29 in Figure 1. At 22:29:45 we get first signs
of sudden dip in altitude received from Ublox F9P. Next 22:30:20 we get second sudden dip in
altitude, at 22:34 we see that spoofing modified altitude of receiver up to 2100m AGL in less
than 5s time, after which telemetry link also has been jammed and ground station stop receive
any data.
Figure 2 is showing data correlation between altitude reported by GNSS receiver, satellites
visibility and EKF vertical variance. We can clearly see pattern, when spoofing is engaged
satellites visibility suddenly drops to about 2-4 satellites and variance error rising.
Figure 1: Altitude interference of GNSS receiver.
Figure 2: Satellite status, EKF vertical variance and altitude.
Figure 3 shows real time EKF status during active spoofing. Almost all lanes of EKF processing
inconsistent data and trying to deny any false data but after sustained amount of time under
influence it cannot longer provide stable flight.
Figure 3: EKF Status during interference.
The objective of this research is to create a method to counteract or eliminate spoofing
signals through the implementation of relatively simple algorithms, if possible, which can be
realized by modifying the widely used UAV flight control software, specifically Ardupilot, thanks
to its open-source code.
5. Automatic data selection algorithm for EKF
As is known, various modifications of the Kalman filter are used in integrated navigation systems
for UAVs. The Extended Kalman Filter (EKF) algorithm is employed to estimate position, speed,
and angular orientation based on measurements from gyroscopes, accelerometers, compasses,
GNSS, velocity, and barometric pressure. The advantage of EKF over simpler complementary
filter algorithms, such as the direct cosine matrix (DCM), is that it effectively rejects data from
measurements containing significant errors through the comprehensive processing of all
available measurements. This makes the aircraft less sensitive to temporary failures of a single
sensor. EKF also allows for the inclusion of measurements from other sensors, such as optical
flow sensors and laser rangefinders, which are used as auxiliary tools in navigation [13].
To prevent interference in the operation of the extended Kalman filter, a modification has
been proposed. The essence of this modification is to prevent unreliable data from affecting the
navigation calculation system by modifying the EKF3 algorithm in the Ardupilot software [25].
The modification is possible because Ardupilot software has an open codebase, fully allowing
the implementation of custom scenarios. The proposed modification involves incorporating a
subroutine in EKF3 to filter values from the GNSS receiver, such as altitude and ground speed.
This comparison of values will occur within the AP_NavEKF3_core [14]. The algorithm provides
for the automatic disconnection of GNSS data from EKF3 when the threshold difference values
between INS (inertial navigation system) and GNSS measurements are exceeded, enabling the
UAV to switch to flight mode based solely on INS data. Re-enabling GNSS can be done either
automatically or by modifying the software of the ground control station, Mission Planner.
Figure 4 presents an option for introducing an additional GNSS state switching command in the
program interface. To implement this function, the software of Mission Planner and Ardupilot
was modified. The command is sent via Mavlink2 message through the telemetry link. Figure 5
shows what sensors are EKF processing.
Figure 4: Manual GNSS disable/enable function.
Magnetometer GNSS Airspeed sensor
Barometric
EKF Accelerometers
sensor
Gyroscopes
Figure 5: Operation during manually disabled GNSS data.
The EKF instantiates multiple instances of the filter called “lanes”. The primary lane is the
one that provides state estimates, rest are updated in the background and available for
switching to. The number of possible lanes is exactly equal to the number of IMUs enabled for
use. Conventionally, each lane uses the primary instance of the Airspeed, Barometer, GNSS and
Magnetometer sensors.
6. Conclusions
The study highlights the susceptibility of UAV navigation systems to external interference,
particularly spoofing, which can lead to aviation disasters or significant deviations from the flight
path. A modification to the EKF3 algorithm in the Ardupilot software is proposed to filter out
unreliable GNSS data, enhancing the safety and reliability of UAV flights. Automatic Data
Selection, modified algorithm allows for the automatic disconnection of GNSS data when
discrepancies with INS measurements are detected, enabling the UAV to rely solely on INS data
for navigation.
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