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Many unmanned aerial vehicles (UAVs) require the installation of automatic dependent surveillance‐broadcast (ADS‐B) transponders to facilitate their daily management. However, since ADS‐B transponders do not have a good security mechanism, they introduce problems including impersonation, spoofing, and private changing of the registration number, making UAV surveillance inconvenient. Radio frequency fingerprinting (RFF) recognition is carried out by utilizing the fact that different electronic devices in a given transponder will affect the transmitted signals, resulting in the formation of RFF features that are unique to the transponder and difficult to forge. Therefore, in this work, a deep learning architecture is proposed to classify UAVs based on ADS‐B signals, and a multi‐head self‐attention RFF recognition model is constructed using variational mode decomposition (VMD) of the preamble data and a transformer encoder for validation. The model achieves better results in terms of noise, Doppler shifting, and multipath effect interference. This method demonstrates that the transformer architecture of natural language processing, combined with appropriate data preprocessing methods, can also be used for RFF recognition, and provides advantages in accuracy and robustness (67.83% vs. 64.17%). A deep learning architecture is proposed to classify unmanned aerial vehicles‐based on automatic dependent surveillance‐broadcast signals, and a multi‐head self‐attention RF fingerprint recognition model is constructed using variational mode decomposition of the preamble data and a transformer encoder for validation. The model achieves better results in terms of noise, Doppler shifting, and multipath effect interference. This method demonstrates that the transformer architecture of natural language processing, combined with appropriate data pre‐processing methods, can also be used for RF fingerprint recognition, and provides advantages in accuracy and robustness (67.83% vs. 64.17%).

Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join the initiative of securing this protocol and propose an efficient detection method to help detect any exploitation attempts by injecting these messages with the wrong information. This paper focused mainly on three attacks: path modification, ghost aircraft injection, and velocity drift attacks. This paper aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-score of 99.14%, and a Matthews correlation coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-score of 99.37%, and a Matthews correlation coefficient (MCC) of 0.988. Eventually, our best outcomes outdo existing models, but we believe the model would benefit from more testing of other types of attacks and a bigger dataset.

Unmanned aerial system (UAS) traffic management (UTM) requires each UAS to communicate with each other and to other stakeholders involved in the operation. In practice, there are two types of wireless communication systems established in the UAS community: automatic dependent surveillance broadcast (ADS-B) and remote identification (Remote ID). In between these two systems, there is ADS-B-like communication which leverages using other types of communications available in the market for the purpose of UTM. This review aims to provide an insight into those three systems, based on the published standard documents and latest research development. It also suggests how to construct a feasible communication architecture. The integrative approach is used in this literature review. The review categorization includes definition, data format, technology used, and research applications, and any remaining issues are discussed. The similarities and differences of each system are elaborated, covering practical findings. In addition, the SWOT analysis is conducted based on the findings. Lastly, multi-channel communication for UTM is proposed as a feasible solution in the UTM operation.

Global aviation emissions have been growing despite international efforts to limit climate change. Quantifying the status quo of domestic and international aviation emissions is necessary for establishing an understanding of current emissions and their mitigation. Yet, a majority of the United Nations framework convention on climate change (UNFCCC)-ratifying parties have infrequently disclosed aviation emissions within the international framework, if at all. Here, we present a set of national aviation emission and fuel burn inventories for these 197 individual parties, as calculated by the high-resolution aviation transport emissions assessment model (AviTeam) model. In addition to CO 2 emissions, the AviTeam model calculates pollutant emissions, including NO x , SO x , unburnt hydrocarbons, black carbon, and organic carbon. Emission inventories are created in aggregated and gridded format and rely on Automatic Dependent Surveillance–Broadcast combined with schedule data. The cumulative global fuel burn is estimated at 291 Tg for the year 2019. This corresponds to CO 2 emissions of 920 Tg, with 306 Tg originating from domestic aviation. We present emissions from 151 countries that have yet to report their emissions for 2019, which sum to 417 TgCO 2 . The improved availability of national emissions data facilitated by this inventory could support mitigation efforts in developed and developing countries and shows that such tools could bolster sector reporting to the UNFCCC.

Space-based automatic dependent surveillance-broadcast (ADS-B) receivers can cover thousands of aircraft, each transmitting 6 ⋅ 2 signals per second. As a result, ADS-B signals are very prone to overlap. When the number of aircraft covered by a receiver reaches 3,000, about 90 % of the signals will be overlapping. Overlapped signals can reduce the decoding accuracy of receivers, so that aircraft information cannot be accurately transmitted to the air traffic control (ATC) surveillance system, hence threatening aviation flight safety. It is necessary to propose signal separation algorithms for space-based ADS-B systems. An orthogonal projection linear constrained minimum variance (OPLCMV) algorithm is proposed, which can separate two signals simultaneously based on the linearly constrained minimum variance algorithm by exploiting the characteristics of overlapped signals. Compared with the state-of-the-art extended projection algorithm and the fast independent component analysis algorithm, the OPLCMV method has a higher decoding accuracy for multiple overlapping signals with a small direction difference of arrival or frequency shift. Moreover, the OPLCMV algorithm has a low computational complexity when the number of overlapped signal sources is less than seven.

Tropospheric propagation has long been recognized as a challenge in satellite communication and GNSS applications, particularly at low elevation angles where multipath, diffraction, and scintillation effects are more pronounced. In this study, the potential of Automatic Dependent Surveillance‐Broadcast (ADS‐B) messages—transmitted by aircraft at 1090 MHz—was explored as a means to monitor tropospheric propagation effects. Over 500,000 ADS‐B messages were collected under varying weather conditions using two ground‐based receivers. A custom methodology was developed to separate propagation‐related impairments from co‐channel interference. It was found that multipath affects signals below 10 degrees, diffraction below 1.1 degrees, and scintillation fading below 0.4 degrees elevation. Through this approach, continuous and low‐cost insights into the behavior of the lower atmosphere were enabled, offering potential benefits as a supplementary technique for GNSS error modeling and satellite‐based atmospheric sensing. This study explores the use of ADS‐B messages to estimate tropospheric effects, effectively isolating atmospheric influences from interference. The approach enables independent lower atmosphere studies and offers a cost‐free supplementary method for satellite applications, benefiting GNSS systems, especially at low elevation angles where multipath and diffraction significantly impact signals.

The current best-estimate of the global annual mean radiative forcing (RF) attributable to contrail cirrus is thought to be 3 times larger than the RF from aviation's cumulative CO2 emissions. Here, we simulate the global contrail RF for 2019–2021 using reanalysis weather data and improved engine emission estimates along actual flight trajectories derived from Automatic Dependent Surveillance–Broadcast telemetry. Our 2019 global annual mean contrail net RF (62.1 mW m−2) is 44 % lower than current best estimates for 2018 (111 [33, 189] mW m−2, 95 % confidence interval). Regionally, the contrail net RF is largest over Europe (876 mW m−2) and the USA (414 mW m−2), while the RF values over East Asia (64 mW m−2) and China (62 mW m−2) are close to the global average, because fewer flights in these regions form persistent contrails resulting from lower cruise altitudes and limited ice supersaturated regions in the subtropics due to the Hadley Circulation. Globally, COVID-19 reduced the flight distance flown and contrail net RF in 2020 (−43 % and −56 %, respectively, relative to 2019) and 2021 (−31 % and −49 %, respectively) with significant regional variations. Around 14 % of all flights in 2019 formed a contrail with a net warming effect, yet only 2 % of all flights caused 80 % of the annual contrail energy forcing. The spatiotemporal patterns of the most strongly warming and cooling contrail segments can be attributed to flight scheduling, engine particle number emissions, tropopause height, and background radiation fields. Our contrail RF estimates are most sensitive to corrections applied to the global humidity fields, followed by assumptions on the engine particle number emissions, and are least sensitive to radiative heating effects on the contrail plume and contrail–contrail overlapping. Using this sensitivity analysis, we estimate that the 2019 global contrail net RF could range between 34.8 and 74.8 mW m−2.

Aviation emissions that are dispersed into the Earth's atmosphere affect the climate and air pollution, with significant spatiotemporal variation owing to heterogeneous aircraft activity. In this paper, we use historical flight trajectories derived from Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry and reanalysis weather data for 2019–2021 to develop the Global Aviation emissions Inventory based on ADS-B (GAIA). In 2019, 40.2 million flights collectively travelled 61 billion kilometres using 283 Tg of fuel, leading to CO2, NOX and non-volatile particulate matter (nvPM) mass and number emissions of 893 Tg, 4.49 Tg, 21.4 Gg and 2.8 × 1026 respectively. Global responses to COVID-19 led to reductions in the annual flight distance flown and CO2 and NOX emissions in 2020 (−43 %, −48 % and −50 % respectively relative to 2019) and 2021 (−31 %, −41 % and −43 % respectively), with significant regional variability. Short-haul flights with durations < 3 h accounted for 83 % of all flights but only for 35 % of the 2019 CO2 emissions, while long-haul flights with durations > 6 h (5 % of all flights) were responsible for 43 % of CO2 and 49 % of NOX emissions. Globally, the actual flight trajectories flown are, on average, ∼ 5 % greater than the great circle path between the origin and destination airports, but this varies by region and flight distance. An evaluation of 8705 unique flights between London and Singapore showed large variabilities in the flight trajectory profile, fuel consumption and emission indices. GAIA captures the spatiotemporal distribution of aviation activity and emissions and is provided for use in future studies to evaluate the negative externalities arising from global aviation.

The vulnerability of Positioning, Navigation, and Timing (PNT) services derived from Global Navigation Satellite Systems (GNSS) makes having a resilient and accurate Alternative PNT (APNT) based on high‐power terrestrial radio sources necessary. The L‐band is very crowded spectral real estate with GNSS, Distance Measuring Equipment (DME) and Air Traffic Control Beacon System (ATCRBS) signals occupying the band from 900–1600 MHz. Thus, as getting new signal and spectrum for APNT would be difficult, we must leverage existing transmissions and infrastructure. The ∼660 Automatic Dependent Surveillance‐Broadcast (ADS‐B) ground stations in the United States represent significant infrastructure that can be leveraged for APNT. However, as ADS‐B was designed for surveillance, it does not inherently possess features necessary to support APNT goals. This paper describes and demonstrates techniques for using ADS‐B Universal Access Transceiver (UAT) signals for PNT. We develop methods to use all ground UAT signals to provide robust, multi‐frequency pseudoranges. We examine the ranging and positioning performance of the UAT signal on the ground and in flight, to demonstrate its ranging accuracy, and hence the timing and synchronization of the station. We demonstrate and analyze navigation using UAT signals, as well as the intra‐system interference challenges of using multiple UAT stations.

This paper investigates the operational patterns and techniques of aerial fire fighting. It is demonstrated that manoeuvrability and endurance are the main characteristics when choosing air tactical aircraft; focus is on load capability for helicopters and air tankers. Water tank filling and deployment techniques are evaluated. Aircraft using pressure deployment systems are found to produce more uniform and heavy coverage in comparison with gravity systems. ADS-B open source data of flight operations and performance was collected. Operational patterns are found to be independent on the size of particular aircraft category (non-amphibious and amphibious air tanker, helicopter, air-tactical aircraft). Effectiveness and cost are modelled using the retardant dropped per operation and the average number of daily missions. The largest aircraft, Type-I helicopters and very large air tankers (VLAT) are found to be the most effective water- and retardant-dropping aircraft. The best cost-to-litre-dropped ratio for water-dropping aircraft is attributed to Type-III helicopters and amphibious Type-III aircraft; for retardant-dropping aircraft, VLAT are most effective. To maximise fire fighting effectiveness, Type-I helicopters and VLAT should be used as far as possible, with pressure deployment systems.