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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.

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 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.

The high-speed movement of aircraft and the rapid topology changes in aeronautical ad hoc networks (AANET) pose significant challenges to existing routing protocols regarding delay and packet delivery rate. To address the issues of high delay and low packet delivery rate at the routing layer, a geographic routing protocol based on path-weighed (GPWR) is proposed. The protocol utilizes the ADS-B system and beacon messages to establish and maintain the neighbor node status table. Then it evaluates the relative forwarding metric using three factors: Euclidean distance, node connectivity, and node load capacity. Following the criterion of maximizing relative forwarding metric, the aircraft with the maximum relative forwarding metric is selected as the next-hop forwarding node. Finally, using the North Atlantic air routes as the simulation scenario, the simulation system was designed and implemented in OMNeT++. Comparative experiments were conducted between GPWR and Greedy Perimeter Stateless Routing (GPSR). Simulation results demonstrate that, compared with GPSR, GPWR reduces the average end-to-end delay by 22.34% and improves the PDR by 6.54%.

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.

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.

Automatic Dependent Surveillance-Broadcast (ADS-B) is a critical technology to transform aircraft navigation by improving safety and overall effectiveness in the aviation industry. However, overlapping of ADS-B signals is a large challenge, especially for space-based ADS-B systems. Existing traditional methods are not effective when dealing with cases that overlapped signals with small difference (such as power difference and carrier frequency difference) require to be separated. In order to generate an effective separation performance of the ADS-B signals by exploring its temporal relationship, Independently Convolutional Gated Recurrent Neural Unit (Ind-CGRU) is presented for encoder–decoder network construction. Experimental results on the dataset SR-ADSB demonstrate that the proposed Ind-CGRU achieves good performance.

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.

The open and shared nature of the Automatic Dependent Surveillance Broadcast (ADS-B) protocol makes its messages extremely vulnerable to various security threats, such as jamming, modification, and injection. This paper proposes a long short-term memory (LSTM)-based ADS-B spoofing attack detection method from the perspective of data. First, the message sequence is preprocessed in the form of a sliding window, and then, an LSTM network is used to perform prediction training on the windows. Finally, the residual set of predicted values and true values is calculated to set a threshold. As a result, we can detect a spoofing attack and further identify which feature was attacked. Experiments show that this method can effectively detect 10 different kinds of simulated manipulated ADS-B messages without further increasing the complexity of airborne applications. Therefore, the method can respond well to the security threats suffered by the ADS-B system.

Because ordinary users cannot quickly obtain the three-dimensional space position of aircraft in the airport terminal area through a two-dimensional display navigation map, the aircraft surveillance simulation system in the airport terminal area based on virtual reality technology is designed. Through analyzing the Automatic Dependent Surveillance-Broadcast (ADS-B) data, the aircraft motion model is established. The three-dimensional aircraft surveillance simulation system for the airport terminal area is realized by Unreal Engine 4 (UE4). Especially, the changes in flight track and flight attitude about aircraft in the three-dimensional scene are designed. The results show that the aircraft movements can be visualized smoothly and correctly based on ADS-B data. It is helpful for users to observe the aircraft of the airport terminal area and analyze the operation status of the plane.