Automatic Dependent Surveillance-Broadcast Research Articles

Cost–Benefit Analysis of Investments in Air Traffic Management Infrastructures: A Behavioral Economics Approach

An important and challenging question for airport operators is the management of airport capacity and demand. Airport capacity depends on the available infrastructure, external factors, and operating procedures. Investments in Air Traffic Management (ATM) infrastructures mainly affect airside operations and include operational enhancements to improve the efficiency, reliability, and sustainability of airport operations. Therefore, they help increase capacity while limiting the impact on the airport infrastructure itself. By reviewing the neoclassical valuation principles for Cost–Benefit Analysis (CBA), we find that it does not consider relevant behavioral economic challenges to conventional analysis, particularly: failure of the expected utility hypotheses, dependence of valuations on reference points, and time inconsistency. These challenges are then incorporated through practical guidelines into the traditional welfare model to achieve a new methodology. We propose a novel CBA behavioral framework for investments in ATM infrastructures to help policy makers and airport operators when faced with a capacity development decision. This is complemented with a practical example to illustrate and test the applicability of the proposed model. The case study evaluates the deployment of Automatic Dependent Surveillance–Broadcast (ADS–B) as an investment aimed at improving ATM operational procedures in the airport environment by providing advanced ground surveillance data. This allows airport operators to discover the causes of taxi congestion and safety hotspots on the airport airside. The benefits of ADS–B are related to enhanced flight efficiency, reduced environmental impact, increased airport throughput, and improved operational predictability and flexibility, thus reducing waiting times. At the airport level, reducing the waiting times of aircraft on the ground would lead to a capacity release and a reduction in delays. The results show that, following a traditional CBA, the investment is clearly viable, with a strong economic return. Including behavioral notions allows us to propose a new evaluation framework that complements this conclusion with a model that also considers inconsistencies in time and risk perception. A positive Net Present Value can turn into a negative prospect valuation, if diminishing sensitivity and loss aversion are considered. This explains the reticent behavior of decision makers toward projects that require robust investments in the short-term, yet are slow to generate positive cash flows. Finally, we draw conclusions to inform policy makers about the effects of adopting a behavioral approach when evaluating ATM investments.

Satellite Attitude Determination Using ADS-B Receiver and MEMS Gyro

Automatic dependent surveillance-broadcast (ADS-B) is a very important communication and surveillance technology in air traffic control (ATC). In the future, more and more satellites will carry out ADS-B technology to perform a global coverage. In order to make full use of the resources in the satellite, this paper proposes a solution for satellite three-axis attitude determination using the ADS-B receiver. The principle of ADS-B-based attitude determination is presented first. On this basis, ADS-B-based methods are employed to solve the problem. To achieve a higher attitude determination precision, gyro is combined with the ADS-B receiver using a multiplicative extended Kalman filter (MEKF). Finally, a simulation is carried out and the result is presented.

Recent Advancements in Controlled Flight into Terrain (CFIT) Prevention

A properly operating aircraft can experience a controlled flight into terrain (CFIT), which is a type of aviation mishap that causes the loss of life and property when it crashes into land or water. The aviation industry has been very concerned about CFIT accidents, and great efforts have been made to avoid them. The most current developments in CFIT prevention are summarised in this research paper. The study analyses existing CFIT prevention technologies, such as enhanced ground proximity warning systems (EGPWS), synthetic vision systems (SVS), and terrain awareness and warning systems (TAWS). It also looks at the possibility of cutting-edge technologies to improve CFIT prevention, such as automatic dependent surveillance-broadcast (ADS-B) and satellite-based augmentation systems (SBAS). The use of CFIT prevention technologies in different aircraft types is discussed in the paper, which also emphasises how effective these systems are at lowering CFIT accidents. It also examines the difficulties and constraints involved in putting CFIT prevention technologies into practice, such as the requirement for appropriate training and the expense of equipment. In order to further improve aviation safety, the paper emphasises the significance of ongoing study and development of CFIT prevention technologies. The use of these technologies can greatly lower the occurrence of CFIT accidents and increase the safety of air travel, especially when combined with pilot training and situational awareness.

Optimization of digital multi-beamforming for space-based ADS-B using distributed cooperative coevolution with an adaptive grouping strategy

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) technology can eliminate the blind spots of terrestrial ADS-B systems because of its global coverage capability. However, the space-based ADS-B system faces new problems such as extremely low Signal-to-Noise Ratio (SNR) and serious co-channel interference, which result in long update intervals. To minimize the position message update interval at an update probability of 95% with full coverage constraint, this paper presents an optimization model of digital multi-beamforming for space-based ADS-B. Then, a coevolution method DECCG_A&A is proposed to enhance the optimization efficiency by using an improved adaptive grouping strategy. The strategy is based on the locations of uncovered areas and the aircraft density under the coverage of each beam. Simulation results show that the update interval can be effectively controlled to be below 8 seconds compared with other existing methods, and DECCG_A&A is superior in convergence to the Genetic Algorithm (GA) as well as the coevolution algorithms using other grouping strategies. Overall, the proposed optimization model and method can significantly reduce the update interval, thus improving the surveillance performance of space-based ADS-B for air traffic control.

Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes

In the next generation modernization plan, the automatic dependent surveillance-broadcast (ADS-B) system plays a pivotal role. However, the ADS-B’s low level of security and its vulnerabilities have raised valid concerns. The main objectives of this paper are to highlight the limitations of legacy ADS-B systems and to assess the feasibility of using Format-preserving (F), Feistel-based encryption (F), with multiple implementation variances (X) (FFX) algorithms, for enhancing ADS-B’s security. The offered solution is implemented in a standard software-defined radio (SDR) ADS-B to be utilized in real-time applications. Furthermore, a new proposed blockchain scheme is used as a secured database to manage the cipher key. The metric of message entropy is used to assess an algorithm’s ability to confuse and diffuse predictable ADS-B messages; correlation and serial correlation of plain data and cipher data are deployed to evaluate the proposed method’s security level. The authors provide both MATLAB simulations and flight test outcomes to demonstrate the feasibility of this approach. Based on our security analysis, ADS-B information can be kept confidential through our scheme. The performance evaluation results reveal that the proposed scheme is achievable, compatible, and efficient for the avionics industry.

A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)

With the exponential increase in the volume of automatic dependent surveillance-broadcast (ADS-B), and other types of air traffic control (ATC) data containing spatiotemporal attributes, it remains uncertain how to respond to immediate ATC data access within a target area. Accordingly, an original multi-level disaggregated framework for airspace, and its corresponding information management is proposed. Further, a multi-scale grid modeling and coding mapping method of airspace information represented by ADS-B is put forth. Finally, tests on the validity of the 4D airspace-temporal grid we named as the GeoSOT-AS framework were conducted across key areas based on the development of an effective data organization method for ADS-B, or an effective algorithm for extracting relevant spatiotemporal data. Experimentally, it was demonstrated that GeoSOT-AS conforms to the existing Chinese specification of civil aeronautical charting and is advantageous for its low deformation and high practicality; furthermore, the airspace grid identification code modeling was less costly, and improved performance by >80% when used for ADS-B data extraction. GeoSOT-AS can thus provide effective reference and practical information for existing airspace data management methods represented by ADS-B and can subsequently be extended to other forms of airspace management scenarios.

A Preliminary Study and Implementing Algorithm Using Finite State Automaton for Remote Identification of Drones

Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the energy required by the drone to use the technology. A finite number of states have been designed to include a larger range of wireless network techniques, enabling the drones to be recognized while they are further away and in remote areas. This is achieved by including other means of RF channels, such as 4G/5G, Automatic Dependent Surveillance-Broadcast (ADS-B), long range Internet of things (IoT), and satellite communications, in the suggested ER-ID algorithm of this study. The introduced algorithm is tested via a case study. The results showed the ability to detect drones using all types of available radio frequency communication systems (RF-CS) while also minimizing the consumed energy. Hence, the authorities can control the licensed drones by using available RF-CS devices, such as Bluetooth and Wi-Fi, which are already widely used for mobile phones, as an example.

Modernizacija i kontrola vazdušnog saobraćaja - implementacija ADS-B sistema 2022 - pregled

Automatic Dependent Surveillance-Broadcast (ADS-B) is a multiparameter surveillance system designed to improve key segments of air traffic: enabling real-time surveillance, raising safety and efficiency levels, and improving flight information and weather services. ADS-B consists of two subsystems, ADS-B Out and ADS-B In. Although only a complete system, ADS-B In/Out provides numerous benefits (additional situational awareness, more efficient oceanic routing, etc.) FAA and EASA only require ADS-B Out (by January and June 2020, respectively), whereby ADS-B In remains optional. Because of its many advantages, ADS-B In/Out will be popular, but there are some weaknesses, which are primarily related to its cyber vulnerabilities due to insufficient authentication and encryption in the applied protocol. In this paper, an overview of the ADS-B system is presented as an aid to understanding the security problems and the different ways of potential attack. In addition, this review deals with the current state of ADS-B deployment and its future perspective and challenges.

COMPARATIVE ANALYSIS OF DATA TRANSMISSION MODES 1090ES AND VDL MODE 4 FOR ADS-B ANTENNAS

The study and analysis of technologies affecting the safety of aircraft flights is relevant and important in a strategic plan. One such technology is Automatic Dependent Surveillance-Broadcast (ADS-B), a technology that allows aircraft pilots and air traffic controllers to receive the most accurate information about aircraft movements. It is currently being implemented in many countries around the world. The VDL-4 message format provides the ability to send message types not described in the standard. This is achieved through the use of spare bits in the fields of service messages. Data communication is described as a presentation layer service that allows information such as the year to be transmitted, or work with the protocols of a self-organizing network to increase its coverage area. The article examines the data transmission lines currently in use for ADS-B. As a result of the work, a comparative analysis of current ADS-B data lines: 1090ES and VDL Mode 4 is presented.

無線信号特徴量を使った航空機位置情報（ADS-B）の検証

This paper describes a position verification method for the aircraft position reporting system called Automatic Dependent Surveillance-Broadcast (ADS-B). The verification method exploits a wireless signal feature, in particular, Time Difference of Arrival (TDOA). In this paper, the principle of the method is firstly described in a generalized form with several possible features. A formulation specific for TDOA is then given. Finally, an experimental result is provided. This ADS-B example is expected to be a useful reference for other position reporting systems.

An ADS‐B signal poisoning method based on generative adversarial network

Automatic dependent surveillance-broadcast (ADS-B) has been widely used due to its low cost and high precision. The deep learning methods for ADS-B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. An ADS-B signal poisoning method based on Generative Adversarial Network is proposed. This method can generate poisoned signals. One of ADS-B signal classification networks is assigned as the attacked network and another one as the protected network. When poisoned signals are fed into these two well-performed classification networks, the poisoned signal will be recognized incorrectly by the attacked network while classified correctly by the protected network. An attack-protect-similar loss function is further proposed to achieve ‘triple-win’ in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show that the attacked network classifies poisoned signals with 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.

ADS-B data usage for aircraft noise and air quality modelling and measurement during specific stages of LTO cycle

The paper is targeted at the analysis of the importance of Automatic Dependent Surveillance-Broadcast (ADS-B) data in the terms of overcoming gaps between aircraft noise and local air quality modelling results and their short or long-term measurements. Presented results based on noise and air pollution measurement campaign at Ukrainian airports describe general peculiarities of the pre-processing and usage of ADS-B data during specific stages of aircraft landing and taking-off (LTO) cycle in the airport. The outcomes could be used for more accurate noise and air pollution exposure assessment and the development of recommendations for noise and local air quality monitoring systems in airports under consideration.

Navigating the skies: examining the FAA’s remote identification rule for unmanned aircraft systems

As technology and innovations in unmanned aerial vehicles progress, so does the need for regulations in place to create safe and controlled flying scenarios. The Federal Aviation Administration (FAA) is a governing body under the United States Department of Transportation that is responsible for a wide range of regulatory activities related to the United States airspace. In a recently published final rule, the FAA addresses several concerns such as the need for a system to identify all aircrafts flying in national airspace, as well as the implementation of a separate system from the prevalent Automatic Dependent Surveillance–Broadcast system to prevent interference with manned aircrafts. Their solution to these concerns is the deployment of remote identification (RID) on all unmanned aircraft systems (UAS) flying under its implied jurisdiction. While US governing agencies retain the use of the word UAS for now, the International Civil Aviation Organization terminology is remotely piloted aircraft systems. The FAA describes the RID implementation as a “ Digital license plate” for all UAS flying in the United States airspace. They outline additional policies including several options for compliance, operating rules, and design and production guidelines for manufacturers. As the September 2023 deadline for compliance draws near, this article highlights possible deployment applications and challenges.

TA-LSTM: A Time and Attribute Aware LSTM for Deep Flight Track Clustering

The rapid development of civil aviation imposes an urgent need for efficient air traffic management. Flight track clustering is the premise and foundation of air traffic control. Flight track points compose time-series data with non-identical point- to-point intervals, and the effective latent representation of track data is key to the flight trajectory clustering. Long Short-Term Memory (LSTM) is an important and popular method to extract the features of trajectory time-series data. However, LSTM ignores the irregular time intervals between track points and fails to effectively distinguish the importance of different attributes at each track point during feature extraction. In addition, the existing methods based on LSTM either ignore the distinct impacts of different attributes at each track point or simply assume that the influences of the previous track points on the subsequent ones decrease with time. In this paper, we propose a flight trajectory feature extraction unit called TA-LSTM (Time and Attribute Aware Long Short- Term Memory) and a flight track clustering model based on TA- LSTM. The flight track clustering model consists of a feature extraction layer and a clustering layer. The feature extraction layer adopts TA-LSTM to obtain the latent representation of flight tracks, which adds a time control gate and an attribute control gate to the standard LSTM unit. The time control gate manipulates the action of each track point on subsequent points to obtain the characteristics of irregular time intervals. The attribute control gat enables LSTM to capture the impact of different attributes on the flight track features at various points. The clustering layer takes the latent representation of flight tracks as the input to obtain the clustering results. The clustering model updates the parameters of TA-LSTM and the cluster centers by minimizing the Kullback-Leibler (K-L) divergence. We conduct experiments using Automatic Dependent Surveillance-Broadcast track data provided by VariFlight. In comparison with popular and state-of-the-art methods, the proposed method obtains superior pe formance in three widely used evaluation metrics, i.e., Silhouette Coefficient, Calinski-Harabaz index and Davies-Bouldin index.

Aircraft Flight Movement Anomaly Detection using Automatic Dependent Surveillance-Broadcast

Automatic Dependent Surveillance-Broadcast (ADS-B) is an aircraft backup radar device that transmits aircraft sensor information via radio frequency. This data can be used to detect aircraft changes that occur significantly or abnormally (anomaly). Anomaly detection in this study aims to reduce and prevent flight accidents by analyzing abnormal data on aircraft flights using the Deep Learning (DL) model. Bidirectional LSTM (Bi-LSTM) and Bidirectional GRU (Bi-GRU) models are proposed as DL models which are implemented on ADS-B data using data mining methods. The data is generated from the ADS-B device that records the plane crash incident and is stored on the Flightradar24 community server. Data containing sensor changes from anomalous aircraft movements are studied for predictability on other flight data. The class breakdown is divided into two, anomaly and normal, based on information on the time span of anomaly occurrences in the accident investigation report of each aircraft using the sliding window technique in the data mining methodology. In evaluation, the confusion matrix measurement method is used to predict predictive analysis of the tested data. The results of the model evaluation performance show that the Bi-LSTM proposed in this study has the best accuracy of 99.44% and the f1-score of 99.51% is slightly superior to the Bi-GRU model. The model in this study can be applied in the ADS-B device to detect aircraft movement anomalies and as material for reviewing technicians in periodic maintenance and measuring the accuracy of the ADS-B device used as a backup radar.

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Specific emitter identification (SEI) is a highly potential technology for physical-layer authentication that is one of the most critical supplements for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristics of hardware circuits, which are difficult to counterfeit. Recently, various deep learning (DL)-based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with a complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intracategory distance and separable intercategory distance, while the latter is realized by an ensemble classifier. Simulation results show that if the number of samples per category is more than 5, the average accuracy of our proposed method is higher than 98%. Moreover, feature visualization demonstrates the advantages of our proposed method in both discriminability and generalization. The code and the dataset can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BeechburgPieStar/FS-SEI</uri> .

Enhancing the performance of the Primary Surveillance Radar using Multilateration

One way to improve the measurements of the PSR (Primary Surveillance Radar) is to utilize the cinematic model of the aircraft (A/C) in a Kalman filter. Another newly developed method would be to implement multilateration using a large number of ground-based ADS-B (Automatic Dependent Surveillance-Broadcast) receivers. Originating in airport surveillance, multilateration grew to become the primary system for ATM (Air Traffic Management) in airspaces without PSR coverage. Given that each of the systems has its own advantages and limitations, we propose an evaluation of an alternative approach that uses data from multiple ADS-B receivers to implement a data fusion algorithm between PSR acquired position and MLAT (Multilateration) estimated position. Among the many ways to implement data fusion, have chosen to analyze two possible solutions: the direct fusion of the two available positions provided by the two systems using a traditional Kalman Filter and a linearization approach for the multilateration solution that does not require position computation. In both cases, these will improve the Kalman filter and lower the position estimation errors. The evaluation takes into consideration the possible sources of inaccuracies and provides sensibility analyses in regards to the number and positioning of ADS-B receivers involved in multilateration. This paper will conclude with a discussion of the computational power required for the two implementations.

A METHOD FOR AUTOMATIC AIRPORT OPERATION COUNTS USING CROWD-SOURCED ADS-B DATA

Airports are tasked with counting and reporting their operations at least yearly. The counts are used at the local and national level to schedule maintenance, for research, and to receive funds, making their accuracy important. Historically, methods for counting operations at non-towered airports have relied on additional equipment at the airport or statistical estimates. In this work, we introduce a method to use crowd-sourced Automatic Dependent Surveillance – Broadcast (ADS-B) data from the OpenSky network to automatically count airport operations and report it separated by takeoffs and landings. We use two airports as case studies – Tulsa International Airport (TUL) and Purdue University Airport (LAF) – and compare the estimated operation counts from the ADS-B data algorithm to numbers reported through the Federal Aviation Administration’s (FAA) Air Traffic Activity Data System (ATADS).

Single antenna ADS-B overlapping signals separation based on deep learning

As a new type of surveillance technology, Automatic Dependent Surveillance-Broadcast (ADS-B) has been regarded as the cornerstone of the next generation air surveillance. However, a potential problem is that multiple ADS-B signals may overlap when they come from different aircraft simultaneously. This paper explores the possibility of using deep learning to separate pulsed ADS-B overlapping signals. Most existing deep learning algorithms separate the overlapping signals by utilizing the difference in time-frequency domains. But all ADS-B signals are transmitted at the same frequency, those deep learning methods based on time-frequency analysis are ineffective. This paper separates pulsed ADS-B overlapping signals directly in the time domain. Firstly, an encoder is used to extract the mixed features of ADS-B overlapping signals in the time domain. Then, Temporal Convolutional Network (TCN) is used to extract the non-overlapping ADS-B signal feature from the output of encoder. Finally, the separated time-domain signals are reconstructed by a decoder. Simulation results show that the separation accuracy of the algorithm based on deep learning is higher than that of the traditional algorithm.

Use of flight tracking data to inform safety deficiencies for general aviation cross-country operations in challenging flying environments

Background: Air carriers, but not general aviation, have long employed in-flight data to identify risks/implement corrective measures for improved safety. Herein, using in-flight data, aircraft (in non-instrument-rated private pilots (PPLs) ownership) operations in two potentially hazardous environments (mountains, degraded visibility) were researched for safety practice deficiencies. Four questions were posed, the first two related to mountainous terrain operations: were aircraft (a) flown with hazardous ridge-level winds, (b) within gliding distance of level terrain? Regarding degraded visibility, did aviators (c) depart with low cloud ceilings (≤3,000 ft.), (d) fly at night away from urban lighting? Methods: The study cohort comprised: (a) single engine aircraft in sole PPL proprietorship (b) registered in Automatic Dependent Surveillance-Broadcast (ADS-B-Out) equipage-required locations prone to low cloud ceilings in three mountainous states. ADS-B-Out data for cross-country flights (>200 nm) were collected. Results: 250 flights (50 airplanes) were tracked (spring/summer 2021). For aircraft transiting areas subject to mountain winds influences, 65% completed one/multiple flights with potentially hazardous ridge-level winds. Two thirds of airplanes traversing mountainous topography would have, for at least one flight, been unable to glide to level terrain with a powerplant failure. Encouragingly, flight departures for 82% of the aircraft were with >3,000 ft. cloud ceilings. Likewise, flights for >86% of the study cohort were undertaken during daylight. Employing a risk scale, operations for 68% of the study cohort did not exceed low-risk (i.e., ≤1 unsafe practice) and high-risk flight(s) (three concurrent unsafe practices) were rare (4% of airplanes). In log-linear analysis, no interactions were evident between the four unsafe practices (p = 0.602). Discussion: Hazardous winds and inadequate engine failure planning were identified as safety deficiencies for general aviation mountain operations. Practical Application: This study advocates for the expanded use of ADS-B-Out in-flight data to inform safety deficiencies/implement corrective measures toward improving general aviation safety.