Unmanned Aircraft Systems (UAS) represent a dynamically developing cluster within the aviation industry. The fields of application for UAS are extensive, and new areas for their use are constantly emerging. Furthermore, the prospect involves an increasingly widespread use of UAS performing autonomous flights.

The feasibility and efficiency of autonomous UAS flights depend on the quality and reliability of aeronautical provision. The primary means for performing autonomous flight are satellite navigation systems (GNSS), which offer a global coverage area, high accuracy, and unlimited throughput capacity. However, there are also a number of problems [1–9] that affect the efficiency of satellite navigation use, the most significant of which is the low interference immunity of GNSS receivers. This is because the signals from navigation satellites (NS) at the input of the receiving antenna have a very low level, and even a low-level external interference is sufficient to suppress the weak signals from the NS. Moreover, there are intentional interference signals that imitate the structure of the NS signals, which can cause the receiver to determine false coordinates and generate false flight trajectories for the UAS, making the use of airspace unsafe for other users. Such interference is called meaconing, and the substitution of GNSS signals is known as spoofing (meaning – to substitute, deceive, falsify) [10–13]. The study [14] provides an analysis of statistical data for 2024, showing an increase in spoofing incidents during regular flights of manned aviation. Therefore, an urgent scientific task arises to determine the presence of spoofing and to counteract it, which will enhance flight safety and the efficiency of aeronautical provision for both manned and unmanned aviation.

The main spoofing scenarios are as follows:

using a jammer to force the GNSS receiver from tracking mode into signal search and acquisition mode, and after resetting the correlator, to feed a false satellite signal to the receiver input to form a false trajectory. This scenario is considered a crude type of spoofing;

using a GNSS receiver on the jammer to obtain identical signal delays and Doppler frequency shift values necessary for generating synchronous spoofing with a higher signal level compared to the levels of signals from the NS, which will allow substitution with a false signal. This type of spoofing is more complex and harder to detect.

According to [15, 16], the following methods for spoofing detection exist:

To use any of these methods, it is necessary to know the structure of specific GNSS receivers and the algorithms they implement for searching, detecting NS signals, and tracking their delay and frequency. In practice, most GNSS receivers installed on UAS are separate modules with a closed structure, which limits the use of most of the mentioned spoofing detection methods.

Study [2] assessed the interference immunity of a multi-system GNSS receiver module like the ATGM336H under the influence of narrowband interference. The structure of this receiver module allows for separate processing of signals from each system. It was experimentally established that when interference is present at the GPS frequency, the module’s performance for the GLONASS system also degrades due to the specifics of intermediate frequency selection in the receiver path.

The GNSS module, connected to the UAS onboard controller, outputs a data packet with coordinates for stabilizing the UAS in space and executing the assigned flight. However, if false GNSS signals are received, the UAS will perform a flight not according to the assigned route, which may be detected by the operator (external pilot) with a significant delay. Therefore, it is necessary to determine the moment when spoofing begins and affects the GNSS receiver in order to promptly notify the operator for making decisions regarding further flight control and excluding information from the satellite receiver from the control loop. For this purpose, a modification in the UAS architecture is proposed, based on the analysis of output data from the satellite receiver. This task can be performed by the flight controller if it has sufficient computational performance, or an additional microcontroller may be required.

This paper presents a methodology for detecting spoofing generated according to the two scenarios considered above, based on the analysis of output data from the ATGM336H receiver module.

The ATGM336H is a compact satellite receiver designed to determine user coordinates, speed, and precise UTC time when operating with signals from GPS, GLONASS, and BeiDou systems. Key features of the receiver module:

The ATGM336H receiver module has a number of characteristics that make it applicable and effective in various fields, including UAS navigation.

The output data from the receiver is a data packet in NMEA0183 format.

“NMEA is a common standard for representing navigation data in text format (ASCII). This protocol is used to transmit GNSS data from the receiver to external devices that are unable to decode the specific manufacturer’s receiver navigation message.”1

The NMEA protocol defines a standard data format that includes information about geographic position, speed, time, course, and other flight parameters. These data are transmitted as text messages containing special codes and fields to identify the type of information.

For developing an algorithm to detect and counter a spoofing attack, it is proposed to analyze data on the parameters of signals received from the NS, such as the signal-to-noise ratio, satellite azimuth and elevation angle, as well as information about the UAS location and flight parameters. This information can be obtained from the NMEA protocol (from GGA, GSV, and RMC messages). The selection of these messages provides a complete set of receiver data and gives a full picture of the navigation conditions.

An analysis of publications by other authors on the use of NMEA protocol data for spoofing detection has been conducted. For instance, study [13] reviews existing methods for determining spoofing, indicating the possibility of analyzing NMEA protocol data. Work [17] presents the main NMEA protocol messages that can be used for spoofing detection and provides results of testing different types of receivers in the presence of spoofing. In [18], a software platform for comprehensive analysis of NMEA messages from two simultaneously operating receivers located at a fixed distance is presented. The authors describe the main methods: checking navigation parameters (speed and altitude); estimating pairwise distances between receivers; checking ephemeris; monitoring time scale offset; monitoring the signal-to-noise ratio (determining the maximum signal level). The work shows that for maritime transport, the most effective method for determining spoofing is the method of estimating pairwise distances between receivers. The method of monitoring the signal-to-noise ratio was not used in this work.

A software interface for the ATGM336H receiver module has been developed to study interference immunity, assess accuracy characteristics, and for a number of other tasks. The interface, whose structural diagram is shown in Figure 1, was developed in the LabVIEW graphical programming environment.

The ATGM336H receiver module is connected to a PC via an RS232 serial port and transmits data to the interface at a specified baud rate for further processing. Relevant information is extracted from the GSV, GGA, GSA, and RMC messages. Specifically: data about satellites in view (satellite ID, azimuth, elevation, and Signal-to-Noise Ratio (SNR), measured in dB‑Hz) are extracted from the GSV messages. The coordinates determined by the receiver module and the time are extracted from the GGA messages. These coordinates then undergo statistical processing to obtain the Root Mean Square Error (RMSE) and plots of coordinate measurement errors. Information about the UAS’s speed and course of movement is extracted from the RMC messages. The developed interface also allows for the processing of GSA messages to extract the GNSS Geometric Dilution of Precision (GDOP) values, which can be used to assess the influence of the geometry of the current satellite constellation on the accuracy of coordinate measurements.

The developed interface also allows for generating commands in the form of PCAS messages to modify the settings of the receiver module. For example, using the appropriate command, one can select the satellite system(s) for the receiver module to operate with, change the output data rate, and more.

The obtained and processed data are subsequently analyzed. Based on this analysis, the receiver module configuration can be adjusted using the generated commands, which can improve its performance in the presence of interference.

The analysis of existing methods for generating false signals indicates that the most straightforward way to perform spoofing currently is by using HackRF One equipment (a software-controlled platform) and an external radio signal amplifier. Consequently, the methodology for detecting meaconing interference must be generalized and include several key criteria for spoofing detection.

As noted earlier, the primary indicator for detecting spoofing onboard a UAS will be the assessment of Signal-to-Noise Ratio (SNR) levels, which allows for the detection of interference that precedes the spoofing signal. Therefore, another indicator for detecting a spoofing signal will be the comparison of the onboard flight plan with the data received from the GNSS receiver. Based on this, the methodology for identifying a spoofing attack consists of two stages:

The determination of threshold Signal-to-Noise Ratio levels for spoofing detection was performed using a CN-3803M satellite signal simulator. One of the advantages of this simulator is its ability to vary the output signal power in the range from −150 dBm to −100 dBm. Under real-world operating conditions, the typical signal level for a receiver is approximately −120 dBm [2, 19].

Signals from GLONASS and GPS navigation satellites were simulated. The receiver’s antenna was placed next to the simulator’s antenna.

A series of experiments was conducted with different output signal levels from the simulator, which is equivalent to changing the SNR in the presence of fixed-level interference. The experiments aimed to determine the maximum and minimum signal levels at the receiver input that lead to extreme SNR values. The minimum SNR level is the threshold value at which the navigation solution is computed with very coarse accuracy.

Under normal receiver operating conditions, a decrease in SNR to the minimum threshold value is unlikely. Firstly, during UAS flight at altitude, the receiver is not subject to multipath effects, signal shadowing, and other degrading factors, so the SNR remains relatively stable. Secondly, in the case of wideband or narrowband interference, a decrease in SNR levels is observed for all satellites (depending on the satellite receiver’s structure) used in the navigation solution. Therefore, it is necessary to evaluate the average SNR value across signals from all navigation satellites included in the solution.

As an example, Figure 2 shows a graph of the relationship between SNR and the input signal level for GPS navigation satellite No. 3. It follows from Figure 2 that a decrease in the input signal level leads to a decrease in SNR.

Table 1 presents the average SNR values for the visible GLONASS and GPS constellations separately, calculated using formula (1):

(1)

where N is the number of visible satellites of the observed system; i is the serial number of a visible satellite of a specific system.

The table shows that decreasing the input signal level below −135 dBm does not lead to a significant degradation of the SNR. At the maximum signal level of −100 dBm, the SNR is approximately 50 dB-Hz for the GPS system and 52 dB-Hz for the GLONASS system, which is not observed under real-world operating conditions. Therefore, an average SNR value for the entire visible constellation of 50 dB-Hz can be used as an upper threshold indicating the presence of meaconing interference.

When receiving signals at critical levels of −135 dBm and −140 dBm, the receiver cannot maintain stable tracking of navigation satellites. This leads to satellites being intermittently included in and excluded from the navigation solution (fig. 3, curve 1 – number of satellites at an input signal level of −135 dBm, curve 2 – at −140 dBm). This primarily affects satellites with the longest ranges (typically those near the horizon).

The Root Mean Square Errors (RMSE) of the receiver’s coordinate determination, evaluated over a 950-second time interval, were as follows:

for an input signal level of −135 dBm: latitude σB = 46.8 m, longitude σL = 204.7 m, altitude σH = 3.8 m;

for an input signal level of −140 dBm: latitude σB = 60.7 m, longitude σL = 92.6 m, altitude σH = 6.3 m;

for an input signal level of −120 dBm (the level under real-world operating conditions of the receiver): latitude σB = 2.2 m, longitude σL = 3.8 m, altitude σH = 0.07 m.

Figure 4 shows the plots of coordinate determination errors (latitude error δB – curve 1, longitude error δL – curve 2, altitude error δH – curve 3) for an input signal level of −135 dBm (fig. 4, a) and −140 dBm (fig. 4, b). Figure 4b shows that due to the instability of horizon satellites, the horizontal coordinate measurement errors vary significantly.

Based on the obtained results, a threshold of 26 dB-Hz should be selected for the minimum signal level. This level is unacceptable under the real-world operating conditions of a GNSS receiver, allowing for the detection of interference.

Thus, to determine the presence of interference that disrupts the satellite navigation receiver’s correlator, a lower threshold of 26 dB‑Hz will be used. In the case of meaconing interference (spoofing), an upper threshold of 50 dB-Hz will be used. Therefore, the following inequality is used as the criterion for detecting interference:

To exclude from the proposed criterion the influence of SNR level reductions from satellites that are entering or leaving the receiver tracking zone, a second criterion must be added. This criterion will be the comparison of the number of satellites used in the GNSS receiver navigation solution against a specified value. The specified value for the number of satellites for each navigation system is chosen as 4, since this is the minimum number required to solve the navigation task and is sufficient when using satellites from other systems when the GNSS receiver operates in a multi-system mode. Therefore, the general criterion for detecting interference impact is as follows:

(2)

The algorithm for detecting interference in navigation receivers is presented in Figure 5.

In the first step of the algorithm, the input data from the receiver is set. This includes SNR values for the three systems (SNRGPS, SNRGLONASS, SNRBeidou), the number of satellites used in the navigation solution (NGPS, NGLONASS, NBeidou), the current UAS coordinates (Bx, Lx, Hx), and the UAS flight plan coordinates (B, L, H), which are defined during the pre-flight preparation stage.

In the second step, the average SNR value for the visible constellation is calculated separately for each system (SNRGPS, SNRGLONASS, SNRBeidou) according to expression (1).

In the third step, the calculated average SNR values for each system are compared with condition (2). If the condition is not met for one of the systems, it is assumed that narrowband interference is present at the input of the receiver on the frequency of that specific navigation system. Consequently, a command is automatically generated for the GNSS receiver to exclude this satellite system from the navigation solution. This will lead to improved coordinate determination accuracy and more stable receiver operation.

If the condition is not met for all systems simultaneously, a decision is made that wideband interference is present at the receiver input. In this case, an informational message is generated for the UAS operator about the inability to further use the GNSS receiver, and a recommendation is provided to take manual control of the UAS flight. This allows for avoiding the first stage of spoofing impact.

If the condition is met for at least one of the systems, the algorithm proceeds to the next verification stage.

During the fourth step, the output UAS coordinates are compared with the flight plan coordinates. This stage allows for the detection of meaconing interference, particularly in cases where malicious actors did not generate interference to reset the GNSS receiver’s correlator or where the satellite signal substitution occurred before the GNSS receiver was powered on.

If this condition is met, no spoofing is detected, and the algorithm repeats from the beginning with updated data from the receiver.

If the condition is not met, an automatic message is generated for the UAS pilot, instructing a transition to manual control of the UAS. Simultaneously, the GNSS receiver is disconnected from the onboard controller to prevent further diversion of the UAS along a false trajectory.

To prevent false triggers of this condition due to positioning errors, a certain tolerance (ΔB, ΔL, and ΔH) is added to each calculated coordinate value from the flight plan. In this work, ΔB and ΔL were set to 10 meters (converted to degrees of latitude and longitude), and ΔH was set to 5 meters, which helps avoid abrupt altitude changes. The UAS flight altitude is determined using the GNSS receiver at altitudes above 60 meters; at altitudes below 60 meters, a barometric altimeter with optical stabilization systems is used for altitude determination.

Thus, the developed algorithm ensures that the GNSS receiver onboard the UAS can continue solving the navigation task under the influence of narrowband interference at an acceptable level on one system’s frequency. It also provides a warning to the operator if wideband interference is present at the GNSS receiver input, or in the case of satellite signal spoofing that has caused the receiver’s output coordinates to deviate from the assigned flight route coordinates

Figure 6 shows the test bench used for testing the interference detection algorithm. The bench includes:

Testing of the developed algorithm was conducted in four stages:

Using the HackRF One module, interference was applied at the GPS L1 frequency of 1575.42 MHz with a power of 30 mW. The interference source antenna was placed next to the antennas of the simulator and the receiver.

Figure 7 shows the graphs of the changes in the average SNRGPS and SNRGLONASS. The results show that the time samples do not coincide, which is associated with the different signal search and acquisition times for the GPS and GLONASS systems.

The sharp drop in SNRGPS to approximately 8 dB-Hz (fig. 7, a) is related to the moment the narrowband interference was generated. During the entire period of interference, unstable tracking of the signals from GPS satellites is observed.

The sharp drop in SNRGLONASS (fig. 7, b) is related to the structure of the ATGM336H module, specifically the passage of one of the harmonics during frequency conversion in the GLONASS channel. However, the SNRGLONASS level remains above the set lower threshold of 26 dB-Hz. Therefore, solving the navigation task using the GLONASS system is possible, and the algorithm generates a command to disable the GPS system signals.

Figure 8 shows the coordinate measurement errors of the ATGM336H module (latitude error δB – curve 1, longitude error δL – curve 2, altitude error δH – curve 3). The figure highlights the moment when the interference was applied, which led to an increase in coordinate determination errors.

After the algorithm generated the command to disable GPS signal reception, a decrease in coordinate measurement errors is observed. This indicates that the GNSS receiver can still be used, albeit with a greater error compared to its accuracy before the interference began.

When narrowband interference is applied at a frequency of 1602 MHz, corresponding to one of the GLONASS frequency channels, a loss of tracking for almost all signals from GLONASS satellites is observed.

Figure 9 shows graphs of the number of satellites N (fig. 9, a, c) used in the solution for each system and the average SNR values (fig. 9, b, d). The marked moments on the graphs for the number of satellites in the solution and for SNRGLONASS (fig. 9, a, b) are associated with a short-term cessation of the interference, which did not significantly affect the overall coordinate measurement accuracy. A significant degradation of SNRGPS is not observed; therefore, the receiver operates with good coordinate determination accuracy (fig. 10, where curve 1 is error δB, curve 2 is error δL, and curve 3 is error δH).

Setting the simulator to a high output signal level (exceeding the level of the real GNSS signal) caused an increase in SNRGPS and SNRGLONASS, exceeding the established threshold of 50 dB-Hz (fig. 11, a, b). Consequently, the algorithm generated the appropriate alert message. This event is displayed on the interface of the software module developed in the LabVIEW environment (fig. 12). In a practical implementation, this message should be sent to the UAS flight controller and then via the C2 (Command and Control) link to the UAS operator’s console.

Even a brief activation of powerful wideband interference leads to the complete suppression of both GPS and GLONASS satellite signals. Figure 13 shows the moment the interference signal was activated and the subsequent drop in SNRGPS and SNRGLONASS below the threshold value. In this case, coordinates from the GNSS receiver become unavailable, resulting in a loss of navigation and stabilization for the UAS. Consequently, the algorithm generates a message for the UAS operator, instructing them to switch to manual control of the UAS.

At this stage, external jammers were not used. It is assumed that false GNSS signals are being fed to the receiver input, which will cause the UAS to deviate from its assigned flight route.

To test the developed algorithm, a scenario was written for the CN-3803M simulator. In this scenario, for the first two minutes, the UAS is at coordinates B = 52.2756°, L = 104.520237°, H = = 30 m, which match the flight plan. Subsequently, the simulator generates signals imitating UAS movement at a constant speed and a course of 0° (spoofing signals), corresponding to movement along a false trajectory.

When spoofing is present, the current UAS coordinates do not match the flight plan coordinates, and the coordinate spoofing indicator is activated (fig. 12).

Under real-world conditions, if this condition is met, a message will be generated for the pilot to take manual control, and data from the GNSS receiver will not be used by the UAS navigation controller. This prevents the UAS from being led astray along a false trajectory.

This work presents a methodology for detecting the impact of narrowband interference, wideband interference, and spoofing signals on a GNSS receiver. An interface for a data processing program was developed for the actual ATGM336H receiver module, enabling subsequent analysis to identify the type of interference. The program interface, based on the developed algorithm, allows for generating alerts to the UAS pilot when the satellite receiver cannot be used, and for selecting a satellite system depending on the frequency of the narrowband jamming. Thus, the interference immunity of the GNSS receiver is enhanced, and the situational awareness of the UAS operator in complex jamming environments is improved.

1. NMEA-0183 Navigation Data Representation Standard. (2024). OrientSystems. Available at: https://orsyst.ru/ blog/nmea (accessed: 23.02.2025).