Air Traffic Surveillance Research Articles

Aircraft Categorization Approach Using Machine Learning to Analyze Aircraft Behavior

The establishment of aircraft categories is a classification technique employed in a variety of aviation disciplines, including design and development, certification, ongoing airworthiness, air traffic management, surveillance, and safety analysis. Traditional approaches rely on manual feature engineering, which can be labor-intensive and ineffective for capturing complex patterns. In this paper, an approach to aircraft categorization using unsupervised machine learning clustering is proposed. The aim of the proposed approach is to be simple in order to be useful and understandable across disciplinary domains; to be scalable to large volumes of air traffic data; and to be adaptable to changes to account for the evolving technological and operational nature of the airspace environment. The application is based on an adapted version of the k-means algorithm that can group aircraft into clusters based on 3D position over time. The approach is validated using real-world, publicly available ADS-B air traffic data, and the results are compared to traditional categorization methods from the field of aircraft certification. The results showed that the model could be used to 1) identify and group aircraft sharing the same flight phase, 2) categorize aircraft with a similar general heading or direction, and 3) distinguish between local regional aircraft operations and longer flight operations. It was also shown that, depending on the use case, the model could be extended to identify more granular behaviors by increasing the K value used to create the model. Overall, the findings demonstrate that leveraging machine learning techniques for aircraft categorization provides an effective, automated, and scalable solution applicable to a wide range of current applications.

ESIT – a digital twin of air surveillance infrastructure

Civil air traffic surveillance and management depends heavily on the use of aircraft transponders replying to radio interrogations from cooperative surveillance systems such as secondary surveillance radars, multilateration and collision avoidance systems. A high number of such surveillance systems combined with a high aircraft density can cause a large quantity of interrogations, therefore having a detrimental impact on transponder behavior and sometimes even causing a loss of replies and thus a deterioration in aircraft surveillance. In order to ensure the safe operation of European air traffic management, the European Commission obligates the member states to take measures to ensure that the transponder load in their air spaces does not exceed a safe level. EUROCONTROL, in its function as network manager responsible for supporting the monitoring, management and coordination of the scarce spectrum resource, has contracted the implementation of a European simulation tool to Austro Control in collaboration with SeRo Systems and the Institute of Microwave and Photonic Engineering as subcontractors. This paper examines the development and use of this tool as well as the functionality it provides.

TTSAD: TCN-Transformer-SVDD Model for Anomaly Detection in air traffic ADS-B data

ADS-B (Automatic Dependent Surveillance-Broadcast) is a key technology in the new generation air traffic surveillance system. However, it is vulnerable to various cyber attacks because it broadcasts data in plaintext format and lacks authentication mechanism. Previous research has rarely considered the application scenarios of ATM (Air Traffic Management) in commercial air transport, and there are the problems of low anomaly detection rate and the non-lightweight model. This paper focuses on ADS-B anomaly detection under the background of ATM. We propose the TTSAD (TCN-Transformer-SVDD Model for Anomaly Detection) model, which aims to address the problems of existing ADS-B anomaly detection methods including inadequate considerations of long-term dependencies and distribution characteristic, the non-lightweight model and the poor adaptive threshold. First, ADS-B time series is input into TCN (Temporal Convolutional Network) prediction module which predicts data in an accurate and quick way using causal convolution and dilated convolution. Then, the predicted ADS-B time series is input into Transformer reconstruction module which reconstructs data accurately and quickly based on Self-Attention and Multi-Head Attention mechanism. Finally, the difference values between the reconstructed values and the real values are input into SVDD (Support Vector Data Description) threshold determination module for an optimal threshold. Experimental results show that the TTSAD model can detect ADS-B anomaly data generated from attacks such as altitude slow offset and DOS (Denial of Service). The TTSAD model is superior to other machine learning methods in terms of recall rate, detection rate, accuracy rate, missing detection rate and false alarm rate. Furthermore, compared with other deep learning methods including LSTM, GRU and LSTM-AE, the TTSAD model has a shorter training time and a lightweight characteristic. This approach guarantees the information security of ADS-B, thereby improving the operational security of ATM.

High Accuracy Next Generation Air Traffic Surveillance System with Potential Cyber-Attacks

High Accuracy Next Generation Air Traffic Surveillance System with Potential Cyber-Attacks

ОЦЕНКА ВОЗМОЖНОСТИ РЕАЛИЗАЦИИ ПРОЦЕССА НАБЛЮДЕНИЯ В РЕГИОНАЛЬНЫХ ЦЕНТРАХ СИСТЕМЫ ОРГАНИЗАЦИИ ВОЗДУШНОГО ДВИЖЕНИЯ РОССИЙСКОЙ ФЕДЕРАЦИИ

Air traffic management (ATM) in regional air traffic control centers is determined by the availability of surveillance equipment that uses the radar principle of determining the coordinates of aircraft. The responsibility zones of the airspace are vast territories where the placement of radar surveillance equipment forming a single control field is not economically feasible. The presence of local areas of space with no air traffic surveillance significantly reduces the safety, regularity and efficiency of flights. The introduction of new technologies that require significantly lower economic costs needs an assessment of the possibility to use them in the relevant regional ATM centers. Therefore, an urgent scientific task is to study the possibility of using multi-position surveillance systems (MPSS) as an assessment of the accuracy characteristics and locations of system elements. The obtained research results can be applied when placing MPSS ground stations to achieve high performance indicators of the surveillance system and air traffic management as a whole.

Original and Low-Cost ADS-B System to Fulfill Air Traffic Safety Obligations during High Power LIDAR Operation

LIDAR is an atmospheric sounding instrument based on the use of high-power lasers. The use of these lasers involves fulfilling obligations with respect to air safety. In this article, we present a low-cost air traffic surveillance solution integrated into an automated operating system for the Rayleigh-Mie-Raman LIDAR of Clermont Ferrand and the statistical elements of its application over more than two years of operation from September 2019 to March 2022. Air traffic surveillance that includes the possibility of shutting off lasers is required by international regulations because LIDAR is equipped with a class four laser that presents potential dangers to aircraft flying overhead. The original system presented in this article is based on software-defined radio. ADS-B transponder frames are analyzed in real-time, and laser emission is stopped during LIDAR operation when an aircraft is detected within a 2 km radius around the LIDAR. The system was accredited in 2019 by the French air traffic authorities. Laser shutdowns due to the detection of aircraft near the Clermont Ferrand LIDAR caused a data loss rate of less than 2% during the period of application.

Performance Evaluation of Pulse Position Modulation Scheme in Automatic Dependent Surveillance Broadcast System

this paper presents a new formula for calculating the Bit Error Rate (BER) and the Packet Error Rate (PER) for binary pulse position modulation scheme which had been used in Automatic Dependent Surveillance Broadcast (ADS-B) system, ADS-B system is used in air traffic surveillance and control. ADS-B system uses the Binary Pulse Position Modulation (BPPM) scheme in the link between the aircraft and the ground station. The results show that the simulated bit-error rate and packet error rate are approximately matches the theoretical one; these results have been obtained for both coherent and non-coherent detection. As a result, mathematical formulas to represent the probability of error for BPPM are also derived for both coherent and no-coherent detection scheme.

Gaussian Mixture Model-Based Online Anomaly Detection for Vectored Area Navigation Arrivals

Identifying anomalous flight trajectories is crucial in airspace operations, as they can potentially lead to safety risks. One of the challenges in identifying abnormal aircraft trajectories in the vectored area navigation (RNAV) terminal airspace is distinguishing between anomalies and trajectories vectored from the structured procedures. Applying existing trajectory pattern identification algorithms to vectored trajectories could create patterns with wide variation within; hence, they cannot effectively discern anomalies from nominal vectored trajectories. In addition, most existing anomaly detection algorithms are developed to detect anomalies in historical air traffic surveillance data, and thus, they cannot be readily applicable for online implementation. To address these problems, an online anomaly detection algorithm for vectored flights in RNAV terminal airspace based on the Gaussian mixture model (GMM) is proposed, which can deal with highly complex airspace operations by incorporating the GMM with dynamic trajectory pattern classification and hybrid trajectory prediction. The proposed algorithm is demonstrated with real air traffic surveillance data at Incheon International Airport (ICN), South Korea.

Handover Strategy Based on Side Information in Air-Ground Integrated Vehicular Networks

The development of the Internet of Things (IoT) and smart cities, combined with the widespread usage of cooperative or independent air traffic surveillance systems such as automatic dependent surveillance-broadcast (ADS-B) bring about novel deployment paradigms in air-ground integrated vehicular networks (AGVN). Therefore, how to make full use of infrastructures, which represented by high altitude platform station (HAPS), and can be orchestrated according to fully use information like ADS-B message, becomes the imminent problem that should be solved. In the view of above problems, in this paper, we propose a novel handover strategy based on side information of the ADS-B for AGVN. Firstly, a practical scenario which use single HAPS as air base station to assist cellular network with multiple users is proposed. Secondly, according to the surveillance platform, side information such as the location, speed, and realtime throughput of HAPS is obtained by all the static base stations using ADS-B system. Thirdly, a modified handover strategy over HAPS based on the side information is used to enhance the service ability of all the network. Simulation results show the fully use of side information can increase the capacity of the AGVN.

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

The vast majority of U.S. airports are not equipped with control towers, which limits their ability to keep records of flight operations. Attempts have been made to use sensor-based technologies to count aircraft operations at non-towered airports; however, they exhibit limited accuracy. To this end, we developed an automated video-based surveillance system capable of detecting general aviation aircraft departure and landing operations, which comprise the vast majority of operations at non-towered airports. The proposed computer vision method is comprised of three modules: aircraft detection, aircraft tracking, and operations count and classification. We explored different camera layouts and state-of-the-art machine learning and deep learning methods to determine the best settings to extract operations trajectory features for operations count and classification. The proposed method was tested at five non-towered airports. Integrating deep-neural-network-based aircraft detectors and image-correlation-based aircraft trackers achieved an accuracy of about 95%, while ensuring processing times that are needed for real-time implementation.

Impact of Structures on the Operation of Air Traffic Radiolocation and Radionavigation Surveillance Systems

The article describes interference affecting the operation of radiolocation and radionavigation devices used in the air traffic surveillance systems, caused by the proximity of building structures. The impact of a hypothetical structure on the operation of primary and secondary air traffic surveillance radars and DVOR/DME beacons was simulated. The results of this simulation are presented in the form of airspace sectors in which false identification of aircraft may occur, and where it will not be possible to identify aircraft or use beacons due to the certain portion of airspace being in the shadow created by the structure. Analysis of the possibility of the PSR radar receiver being blocked by a strong signal reflected from a nearby building was performed as well.

Tracking of Aircrafts Using Software Defined Radio (SDR) With An Antenna

Automatic Dependent Surveillance-Broadcast (ADS-B) is one in all the favoured technologies employed in air traffic surveillance. The ADS- B uses a band of 1090 MHz. ADS-B is attended with the prevailing radar-based technologies to locate aircraft. The Next Generation Air Transportation System (NGATS) conflicts can be detected and resolved by the coexistence of radar systems and ADS-B. Here we tend to track the aircraft using Software Defined Radio, hence the complexness and the value of ADS-B system implementation is drastically reduced. SDR can receive multiple numbers of aircraft information like altitude, latitude, longitude, speed, and direction in real-time and displayed by using an appropriate antenna. The usage of SDR maximizes the coverage of data with accuracy and may accomplish timely.

On Jamming Attacks in Crowdsourced Air Traffic Surveillance

Wireless jamming has been a long-standing issue both in critical infrastructure research and real-world applications, with particular importance in systems that deal with long communication distances and low received signal strength. Air traffic control is one such system, experiencing a rising number of reports of outside interference with the underlying wireless technologies. In this article, we discuss the particular issue of malicious wireless jamming on crowdsourced networks of low-cost ADS-B receivers, which increasingly support modern air traffic management. Using both simulation and laboratory trials, we first show the practical impact of reduced reception and coverage of typical receivers in a crowdsourced sensor network. Following this, we investigate network-wide countermeasures based on redundant coverage, which can defend at most 50.74% of the evaluated real-world network. To improve jamming resilience in nonredundant areas, we analyze and implement a low-cost multichannel receiver and show that it can effectively recover up to 50% of the messages even under heavy jamming conditions.

Temporal logic learning-based anomaly detection in metroplex terminal airspace operations

The airspace system is a complex dynamical system with complicated controlled interactions between its constituent subsystems – terminal airspace, en-route airspace, and ground. Of these, air traffic management in the multi-airport (metroplex) terminal airspace is one of the most complicated subsystems to manage, especially due to the interactions between proximal airports. Analyzing anomalous behaviors in the metroplex is emerging as a key problem in understanding air traffic management complexity and safety. Although physics-based approaches have been studied in-depth for this application, newfound interest has been observed to use recorded time-series air traffic surveillance and airport operations datasets for this purpose. In this paper, we propose a machine learning-based anomaly detection algorithm that generates mathematical models to detect anomalies in metroplex operations. Several machine learning algorithms have been developed to detect anomalies using only air traffic surveillance data, but there is a significant scope of improvement by including airport operational characteristics as well, since integrating such closely-controlled metroplex operational datasets allows the developed models to effectively detect anomalies. The key contribution of this paper is in allowing anomaly detection models to recursively update so as to adapt to changes in metroplex operations. The proposed algorithm is demonstrated with real air traffic surveillance and airport operations datasets at LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for operations in the New York metroplex.

ADS-B anomaly data detection model based on VAE-SVDD

As a key technology of the new generation air traffic surveillance system, ADS-B (Automatic Dependent Surveillance-Broadcast) is vulnerable to cyber security challenges because it lacks data integrity and authentication mechanism. For detecting ADS-B data attacks accurately, an anomaly detection model is proposed which fully considers temporal correlations and distribution characteristics of ADS-B data. First, VAE (Variational AutoEncoder) is used to reconstruct ADS-B data so that the reconstructed values can be obtained. Then, for the sake of solving the adaptive problem of anomaly detection threshold, the difference values between the reconstructed values and the actual values are put into SVDD (Support Vector Data Description) for training, and a hypersphere classifier that can detect ADS-B anomaly data is obtained. In addition, in order to prevent overfitting and underfitting, appropriate reconstructed values are selected which can reduce FPR (False Positive Rate) and FNR (False Negative Rate) of anomaly detection. Experiments show that the VAE-SVDD model can detect ADS-B anomaly data which is generated by attacks such as random position deviation and constant position deviation. Moreover, compared with other machine learning methods, this model is not only more adaptable, but also has a lower FPR and FNR.

Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace

For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms.

A Hybrid RCS Reduction Method for Wind Turbines

Wind turbine towers produce significant scatterings when illuminated by radars. Their reflectivity affects air traffic control, military surveillance, vessel tracking, and weather data sensing processes. Reducing the radar cross-section (RCS) of wind turbines is an essential task when building wind farms. It has been proved that round and bumpy structures can scatter radar waves and reduce the RCS of a reflector. Other research showed that taper towers generate smaller radar returns than cylindrical towers. In this research, we combine both strategies to devise a more effective method for designing wind turbine towers in the hope that their RCS can be further reduced. The test results reveal that the proposed method out-performs current reshaping methods. Wind turbine towers possessing taper shapes and periodic surface bumps deflect incident electromagnetic waves to insignificant directions. Thus, radar returns in the back-scattering directions decrease. Other experiments also verify that the proposed method maintains its effectiveness for radar waves with varying frequencies and polarization.

Passive Forward-Scattering Radar Using Digital Video Broadcasting Satellite Signal for Drone Detection

This paper presents a passive radar system using a signal of opportunity from Digital Video Broadcasting Satellite (DVB-S). The ultimate purpose of the system is to be used as an air traffic monitoring and surveillance system. However, the work focuses on drone detection as a proof of the concept. Detecting a drone by using satellite-based passive radar possess inherent challenges, such as the small radar cross section and low speed. Therefore, this paper proposes a unique method by leveraging the advantage of forward-scattering radar (FSR) topology and characteristics to detect a drone; in other words, the system is known as a passive FSR (p-FSR) system. In the signal-processing algorithm, the empirical mode decomposition (EMD) is applied to the received signal to extract the unique feature vector of the micro-Doppler frequency from the drone’s rotating blades. The paper highlights the p-FSR experimental setup and experiment campaign to detect drones. The experimental results show the feasibility of the p-FSR using a signal transmitted from a satellite to detect flying drone crossing the forward-scatter baseline between the satellite and ground station.

Threat model and construction strategy on ADS‐B attack data

With the fast increase in airspace density and high-safety requirements on aviation, automatic dependent surveillance-broadcast (ADS-B) is regarded as the primary method in the next generation air traffic surveillance. The ADS-B data is broadcast with the plain text without sufficient security measures, which results in various attack patterns emerging. However, in terms of constrictions with laws and regulations, ADS-B attack data is difficult to collect and obtain, which is essential for data security research studies. To deal with the absence of ADS-B attack data in real environments, the construction strategy on ADS-B attack data is proposed. For construction fidelity, ADS-B data features are analysed and modelled at first. Then the popular and classical attack patterns on ADS-B data are analysed to establish threat models. Based on the original ADS-B data sets, the construction strategy is designed to focus on attack target selection, key parameter determination, and mixture strategy, reproducing the attack intentions. The constructed ADS-B attack data sets are hybrid data sets including the normal and attack data. By simulation analyses, the feasibility and availability of the construction strategy were validated with real ADS-B data.

Actual TDoA-based augmentation system for enhancing cybersecurity in ADS-B

Currently, cybersecurity and cyber resilience are emerging and urgent issues in next-generation air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast (ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration (MLAT) techniques that use Time Differences of Arrivals (TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System (ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.