Automatic Dependent Surveillance-Broadcast Research Articles

Developing Capacity Estimation Metrics for Airports Accommodating Smaller Aircraft Using Locally Collected Automated Dependent Surveillance-Broadcast Data

Airport operators need good capacity estimates for many purposes, including to bolster applications for funding support for capacity improvements. Based on a recognized need to enhance models for estimating the operating capacity of airports serving a user base of smaller aircraft, such as small general aviation airports, this paper describes research conducted to leverage automated dependent surveillance-broadcast (ADS-B) data to develop aircraft performance characteristics for use as inputs to future small airport capacity models. The research addresses this challenge by constructing home-built ADS-B data collection units using a Raspberry Pi-based hardware platform and an Amazon Web Services-based cloud architecture housing the study’s PostGreSQL database, and python-based codebase. The work was focused at three participating airports: The Ohio State University Airport in Columbus, OH (KOSU); College Park, MD (KCGS); and Republic Airport in Farmingdale, NY (KFRG). The hardware units were deployed at three study airports, collecting more than 90 million individual ADS-B messages transmitted by aircraft operating within the vicinity of the study airport. Models were developed to clean, process, and assess the data, and leveraged to determine aircraft performance characteristics within these environments. Initial work focusing on assessing the fleet mix, approach speeds, aircraft separation in arrival streams, and runway occupancy times was performed. Findings from this research revealed a very high accuracy and richness of collected and processed data, and performance metrics that were reasonable, valid, and applicable for use as inputs to future airport capacity models.

A Satellite Selection Strategy of SURF IA in Airport Intelligent Monitoring

The implementation of Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA) monitoring in airports improves the safety and efficiency of airport surface operation. Navigation accuracy category for position (NACP), navigation accuracy category for velocity (NACV), and other data are obtained from Automatic Dependent Surveillance-Broadcast (ADS-B) IN, and these data will be the only data source of SURF IA. In this paper, we use the Beidou System (DBS) and Global Positioning Symbol (GPS) dual system as the positioning data source of the ADS-B. In cases where all visible satellites meet the performance requirements of the SURF IA, particle swarm optimization (PSO) and a new particle swarm optimization (APSO) are used to screen the integrated navigation satellites to meet the requirements of SURF IA by using fewer satellites, and the satellite selection ability of the two is compared. The simulation results show that under the same conditions, by using fewer navigation satellites can meet the minimum monitoring performance requirements for the implementation of SURF IA operation. And the star selection ability of APSO is better. This improves the performance of airport surface intelligent monitoring.

Flight Fare Prediction using Random Forest Algorithm

Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.

Global Civil Aviation Emissions Estimates for 2017–2020 Using ADS-B Data

Aviation is a growing source of atmospheric emissions impacting the Earth’s climate and air quality. Comprehensive assessments of the environmental impact of this industry require up-to-date, spatially resolved, and speciated emissions inventories. We develop and evaluate the first such estimate of global emissions from aircraft operations for the years 2017–2020. Aircraft activity data, based on flights registered by networks of aircraft Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry receivers, are used together with the Base of Aircraft Data (BADA) 3.15 aircraft performance model and the International Civil Aviation Organization Engine Emissions Databank to estimate spatially resolved fuel burn and emissions of , , (), (), CO, unburnt hydrocarbons (HC), and nonvolatile particulate matter (nvPM). We calculate that 937 Tg of and 4.62 Tg of were emitted by aircraft in 2019, and quantify the evolution of the fleet average emission indices over time. Owing to impacts from COVID-19, we estimate a 48% lower fuel burn, resulting in 463 Tg less and 2.29 Tg less emitted in 2020 than what would be otherwise expected. We conclude that ADS-B is a viable source of data to generate global emissions estimates in a timely and transparent manner for monitoring and assessing aviation’s atmospheric impacts.

A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices.

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.

Hybrid 4-Dimensional Trajectory Prediction Model, Based on the Reconstruction of Prediction Time Span for Aircraft en Route

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.

Deep flight track clustering based on spatial–temporal distance and denoising auto-encoding

The rapid development of the aviation industry imposes an urgent need for airspace traffic management. Meaningful clustering of flight tracks is of paramount importance for efficient operation and management of increasingly complex aerial space and traffic. Two key components exist in track clustering: similarity metric and clustering method. Most of the existing studies on track similarity metrics only consider the spatial coordinates of the track points without taking into consideration of the rich information of the track data, such as flight heading and flight speed, on the measurement of track similarity. In addition, temporal properties and the derived features of the flight tracks shall be utilized to reveal the underlying patterns and overcome distortions from noise. In this paper, we propose a track similarity based on the spatial–temporal characteristics of flight tracks and a Deep Temporal Clustering method using a denoising autoencoder. Our proposed method employs the Deep Temporal Denoising Auto-encoding network to extract the latent representations of the track sequences. By extending the idea of k-means clustering, Deep Temporal Clustering groups the flight tracks with a Time Clustering Layer. Experiments are conducted using Automatic Dependent Surveillance-Broadcast track data. In comparison with classical and state-of-the-art methods, among all cases, our Deep Temporal Clustering method achieved a much-improved performance of more than 57.3%. When we introduce noise to the track records and increase its magnitude, the performance of our method degrades but the trend slows down as the noise magnitude increases. The change is less than 7% and, in some cases, is close to zero, which demonstrates the robustness of our method to noise.

Hybrid N-Inception-LSTM-Based Aircraft Coordinate Prediction Method for Secure Air Traffic

With the rapid growth of the number of flights, the traditional radar system has been unable to meet the needs of flight supervision. At the same time, it also puts forward higher requirements for air traffic management (ATM). Automatic dependent surveillance-broadcast (ADS-B) is a promising technology in the next generation of air traffic control (ATC). However, the openness of ADS-B system brings the opportunities for terrorists to tamper with data. In this paper, we propose a novel aircraft coordinate prediction hybrid model based on deep learning. The proposed model combines inception modules and long short-term memory (LSTM) modules. Inception modules are used to extract the spatial features of dataset, and LSTM modules are used to extract the temporal features of dataset. In addition, we use the ADS-B signal strength instead of its specific information to obtain aircraft coordinates. Signal strength is not easily tampered with, but it carries limited information. Therefore, this scheme sacrifices a certain precision for reliability. Inception modules and LSTM modules are combined in different ways to perform experiments on the real-world ADS-B datasets from OpenSky network. The experimental results show that the proposed 2-Inception-LSTM is the local optimal model. The prediction error is within 10 km. It can be suitable for situations where the positioning accuracy of aircraft coordinates is not pursued, but the positioning reliability must be guaranteed.

Spatiotemporal analysis to understand overflight travel patterns at HawaiI Volcanoes National Park

Overflights, especially low-level flights, have a measurable impact on the acoustic environment of the areas they traverse. This is of particular concern to protected areas with a mission of protecting natural resources, cultural resources, and the visitor experience. Yet, understanding overflight travel patterns at parks and protected areas has proven difficult to determine. Recent technology makes it possible to collect accurate overflight data allowing researchers to quantify travel patterns across a protected area. Location data, including x, y, and z data, can be used to quantitatively assess effects of overflights and to inform solutions to protect resources and the visitor experience. Therefore, the purpose of this study was to spatiotemporally quantify overflight travel patterns at Hawai'i Volcanoes National Park using data derived from Automatic Dependent Surveillance-Broadcast (ADS-B) technology. Location data, a digital grid, and hot spot clustering were used to identify spaces with a statistically-significant high volume of overflights. Some of these spaces overlap with terrestrial visitor attraction locations, suggesting potential for degraded visitor experience. Specifically, results indicate a spatial trend of low-flying overflights along the eastside of the park, a high volume of overflights near Nāpau Trailhead and Nāpau Crater, and spatial clustering of overflights varies across hour of the day. The described methods provide a foundation for future research and the results are useful for informing the designation of lateral and minimum altitude parameters for low-level overflights such as air tours.

Simulation System of Aircraft Surveillance in Airport Terminal Area

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.

UAS Traffic Management Communications: The Legacy of ADS-B, New Establishment of Remote ID, or Leverage of ADS-B-Like Systems?

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.

CAE: Contextual auto-encoder for multivariate time-series anomaly detection in air transportation

The Automatic Dependent Surveillance-Broadcast protocol is one of the latest compulsory advances in air surveillance. While it supports the tracking of the ever-growing number of aircraft in the air, it also introduces cybersecurity issues that must be mitigated e.g., false data injection attacks where an attacker emits fake surveillance information. The recent data sources and tools available to obtain flight tracking records allow the researchers to create datasets and develop Machine Learning models capable of detecting such anomalies in En-Route trajectories. In this context, we propose a novel multivariate anomaly detection model called Contextual Auto-Encoder (CAE). It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase (e.g. climbing, cruising or descending) during its training. To illustrate the CAE’s efficiency, an evaluation dataset was created using real-life anomalies as well as realistically crafted trajectory modifications, with which the CAE as well as three anomaly detection models from the literature were evaluated. Results show that the CAE achieves better results in both accuracy and speed of detection. The dataset, the models implementations and the evaluation results are available in an online repository, thereby enabling replicability and facilitating future experiments.

Instrument flying infrequency and weather decision-making for general aviation pilots -implications for flight safety in degraded visibility

IntroductionInstrument-rated private pilots (PPL-IFRs) are trained to fly in degraded visibility (where visual references are lost) and, towards maintaining proficiency, required to complete 6 instrument approaches in as many months. Nevertheless, operations in such weather carry an elevated risk of a fatal accident. Two hypotheses were advanced: (i) the majority of PPL-IFRs complete fewer than 6 instrument approaches/6 months and (ii) that a subset of such aviators still depart into such weather. Methods: Instrument approaches for a group of light aircraft in single PPL-IFR ownership (aircraft/owners) in the state with the largest count of single piston engine aircraft (Texas) was determined using Automatic Dependent Surveillance-Broadcast (ADS-B) data. Departure aerodrome weather flight category was per the Iowa State University database. IMC accidents were identified from the National Transportation Safety Board database and rates determined using GA fleet time. Statistical testing used Poisson Distribution, Chi-Square and Mann-Whitney U tests. Results: Over the Aug 2020-Mar 2021 period, 1,684 flights involving 106 aircraft/owners were ADS-B-tracked. Of these aircraft/owners, 81.2% completed < six approaches (median = 1.5). Importantly, for aircraft/owners completing < six approaches in the preceding 6 months, 24% departed into obscuration. Conclusions: This study demonstrates that, for the state most populous for reciprocating engine aircraft, the majority of aircraft in single PPL-IFR ownership fail to complete 6 in-flight instrument approaches/6 months. Nevertheless, some of these aircraft/owners elect to undertake flights in degraded visibility. From a practical perspective, PPL-IFRs unable to maintain instrument currency should be cautioned on the risks of operating in degraded visibility.

Satellite coverage traffic volume prediction using a new surrogate model

The space-based Automatic Dependent Surveillance-Broadcast (ADS-B) can significantly improve aircraft traffic efficiency and safety but suffers from the issue of signal collision. A potential solution for this limitation is employing an adaptive ADS-B antenna, e.g., the phased array antenna, which can adjust its coverage area in real-time during the satellite movement according to the satellite coverage traffic volume (SCTV). This paper proposes a new surrogate modeling technique, i.e., subregion radial basis function (RBF), and applies the proposed surrogate to predict the SCTV so that the adaptive strategy for the ADS-B antenna can be designed. Instead of training one model using all training samples as the classical RBF does, the proposed subregion RBF approach first partitions the input space into multiple fuzzy regions, then trains a RBF model in each region, and finally integrates the multiple subregion RBF models in a weighted average manner. By using this partition of unity strategy, the proposed method can greatly reduce the computational complexity of training models meanwhile maintain the interpolation property and high accuracy of RBF models. Further, the proposed subregion RBF is applied to construct a surrogate for the SCTV model to guide the design of the adaptive strategy for the space-based ADS-B. Simulation results show that compared with the classical and state-of-the-art methods, the proposed method has arrived a better balance between predictive accuracy and computational cost. In the simulation scenarios, it is also observed that by using the adaptive strategy, the detection probability of ADS-B receiver can always be greater than 95% and nearly 1/3 of power consumption can be saved. The simulations demonstrate the effectiveness of the proposed method.

ISTSat‐1, a space‐based Automatic Dependent Surveillance‐Broadcast demonstration CubeSat mission

SummaryThe use of space‐based Automatic Dependent Surveillance‐Broadcast (ADS‐B) receivers to extend aircraft surveillance beyond line‐of‐sight communication has been discussed since the late 2000s and has been demonstrated in various missions. Although recent demonstrations have relied on small CubeSat platforms, the use of the smallest possible form factor has not been explored. This paper describes development of a spacecraft representing the first use of a 1 U CubeSat platform for aircraft tracking. To comply with the platform's limited power budget, the receiver departs from the typical application of FPGA boards to decoding ADS‐B signals and instead employs a general microprocessor. The spacecraft also features some innovations over traditional CubeSat platforms, such as distributed system‐level software amenable to extensive code reuse, redundant communications mechanisms, and software‐based radiation mitigation strategies. Having designed, built, and integrated all subsystems, as well as performed multiple test campaigns at system and subsystem level, the team now aims at completing formal functional and environmental testing before undergoing the launch campaign, foreseen for 2022.

Estimation of aircraft fuel consumption by modeling flight data from avionics systems

Accurate and economic estimation of aircraft fuel consumption is fundamental for optimizing aviation operations, including emission reduction, flight route planning, and fuel management. Numerous literature presented mathematical models to estimate aircraft fuel consumption but often neglected the challenges of applying those methods in aviation operations. This paper explores a novel strategy to estimate aircraft fuel consumption by modeling flight data from onboard flight data recorder (FDR) and automatic dependent surveillance – broadcast (ADS-B). The Classification and Regression Tree (CART) and Neural Networks (NNs) are adopted for modeling. CART and NN models are developed using FDR data; ADS-B data are used to assess the model performance. The result indicates that the CART model performs better when inputs contain errors and missing values, and the ADS-B data could be used to estimate aircraft fuel consumption as a less-expensive and more convenient strategy compared to the FDR data.

How does COVID-19 affect the implementation of CORSIA?

This paper investigates the impacts of COVID-19 on the implementation of Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). By using the Automatic Dependent Surveillance-Broadcast (ADS-B) aviation data, the forecast methods of Gompertz and Logistic curves and four COVID-19 scenarios, we find the following results. First, the international aviation activities of developing countries are on the track of rapid growth, while the trends of developed countries are relatively slow or even close to saturation. Second, our results provide retrospective support for the decision of the ICAO Council to revise the implementation baseline of CORSIA. The adjustment of the baseline has saved countries considerable purchasing offsetting costs, especially for China, the United States, the United Arab Emirates, and the United Kingdom. Third, although the adjustment of the baseline can lower the economic pressure of the global aviation industry, CORSIA will still bring considerable financial burden to international aviation enterprises.

On the (In)Security of 1090ES and UAT978 Mobile Cockpit Information Systems–An Attacker Perspective on the Availability of ADS-B Safety- and Mission-Critical Systems

Automatic dependent surveillance-broadcast (ADS-B) is a key air surveillance technology and a critical component of next-generation air transportation systems. It significantly simplifies aircraft surveillance technology and improves airborne traffic situational awareness. Many types of mobile cockpit information systems (MCISs) are based on ADS-B technology. MCIS gives pilots the flight and traffic-related information they need. MCIS has two parts: an ADS-B transceiver and an electronic flight bag (EFB) application. The ADS-B transceivers transmit and receive the ADS-B radio signals while the EFB applications hosted on mobile phones display the data. Because they are cheap, lightweight, and easy to install, MCISs became very popular. However, due to the lack of basic security measures, ADS-B technology is vulnerable to cyberattacks, which makes the MCIS inherently exposed to attacks. Attacks are even more likely for the MCIS, because they are power, memory, and computationally constrained. This study explores the cybersecurity posture of various MCIS setups for both types of ADS-B technology: 1090ES and UAT978. Total six portable MCIS devices and 21 EFB applications were tested against radio-link-based attacks by transmission-capable software-defined radio (SDR). Packet-level denial of service (DoS) attacks affected approximately 63% and 37% of 1090ES and UAT978 setups, respectively, while many of them experienced a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system crash</i> . Our experiments show that DoS attacks on the reception could meaningfully reduce transmission capacity. Our coordinated attack and fuzz tests also reported worrying issues on the MCIS. The consistency of our results on a very broad range of hardware and software configurations indicate the reliability of our proposed methodology as well as the effectiveness and efficiency of our platform.

GDL90fuzz: Fuzzing - GDL-90 Data Interface Specification Within Aviation Software and Avionics Devices–A Cybersecurity Pentesting Perspective

As the core technology of next-generation air transportation systems, the Automatic Dependent Surveillance-Broadcast (ADS-B) is becoming very popular. However, many (if not most) ADS-B devices and implementations support and rely on Garmin’s Datalink 90 (GDL-90) protocol for data exchange and encapsulation. This makes it essential to investigate the integrity of the GDL-90 protocol especially against attacks on the core subsystem availability, such as denial-of-service (DoS), which pose high risks to safety-critical and mission-critical systems such as in avionics and aerospace. In this paper, we consider GDL-90 protocol fuzzing options and demonstrate practical DoS attacks on popular electronic flight bag (EFB) software operating on mobile devices. Then we present our own specially configured avionics pentesting platform and the GDL-90 protocol. We captured legitimate traffic from ADS-B avionics devices. We ran our samples through the state-of-the-art fuzzing platform American Fuzzy Lop (AFL) and fed the AFL’s output to EFB apps and the GDL-90 decoding software via the network in the same manner as legitimate GDL-90 traffic would be sent from ADS-B and other avionics devices. The results showed worrying and critical lack of security in many EFB applications where the security is directly related to the aircraft’s safe navigation. Out of the 16 tested configurations, our avionics pentesting platform managed to crash or otherwise impact 9 (56%). The observed problems manifested as crashes, hangs, and abnormal behaviors of the EFB apps and GDL-90 decoders during the fuzzing test. Our developed and proposed systematic pentesting methodology for avionics devices, protocols, and software can be used to discover and report vulnerabilities as early as possible.

Comparison of ADS-B Verification Methods: Direct TDOA and MLAT

Automatic Dependent Surveillance—Broadcast (ADS-B) is an emerging means of aeronautical surveillance for air traffic control. Aircraft periodically broadcast positional updates to ground stations. Although ADS-B outperforms traditional radars in terms of accuracy and update rate, positional verification—a technique used to check the validity of the position report—is necessary to counter anomalies. In this study, two different methods were compared when the ground stations measure time difference of arrival (TDOA). One is direct; the test statistic is essentially the difference between the measurement and a prediction calculated from the position report. Another method is multilateration (MLAT)-based and two-step; the emitter position is firstly estimated, whereupon the difference between the estimated and reported positions constitutes the test statistic. As a result of the comparison, a performance difference, which depending on the number of receivers, was revealed. This is an useful suggestion for implementing ADS-B when the existing multilateration infrastructure is exploited.