Air Traffic Control System Research Articles

Launching Point Estimation Using Inverse First-Order Pitch Programming

This paper presents an estimation of the launching points of unmanned aerial vehicles (UAVs) equipped with boosters. When UAVs are detected and tracked by sensors such as radars, the fast identification of vehicle launching points is needed for air traffic control and defense systems. When UAVs in a boosting phase are controlled by first-order pitch programming, this paper leverages inverse first-order pitch programming to analytically solve launching points. Furthermore, a two-point robust measurement selection (T-RMS) scheme is developed to reduce errors such as random noise and bias by utilizing multiple moving average filters. The proposed work is verified by various simulation results.

Aircraft conflict resolution with trajectory recovery using mixed-integer programming

AbstractTo guarantee the safety of flight operations, decision-support systems for air traffic control must be able to improve the usage of airspace capacity and handle increasing demand. This study addresses the aircraft conflict avoidance and trajectory recovery problem. The problem of finding the least deviation conflict-free aircraft trajectories that guarantee the return to a target waypoint is highly complex due to the nature of the nonlinear trajectories that are sought. We present a two-stage iterative algorithm that first solves initial conflicts by manipulating their speed and heading control and then identifying each aircraft’s optimal time to recover its trajectory towards their nominal one. We extend existing mixed-integer programming formulations by modelling speed and heading control as continuous variables while recovery time is treated as a discrete variable. We develop a novel iterative approach which shows that the trajectory recovery costs can be anticipated by inducing avoidance trajectories with higher deviation, therefore obtaining earlier recovery time within a few iterations. Numerical results on benchmark conflict resolution problems show that this approach can solve instances with up to 30 aircraft within 10 min.

Algorithms of Cross-Domain Redundancy Management for Resilient of Dual-Priority Critical Communication Systems

The paper presents models for managing cross-domain redundancy to enhance the reliability of two priority communication channels within critical infrastructure systems. Employing Markov chain models, the paper analyzes the impact of two distinct redundancy management strategies: a unified reserve pool and a separate pool approach with cross-domain resource sharing. The study introduces reliability improvement factors to quantify system performance, exploring their dependency on the number of additional redundant elements, their inherent reliability, and the chosen strategy for managing cross-domain redundancy. An air traffic control system serves as a case study of the application of the proposed management algorithms. Results indicate that the integration of resources from different priority domains significantly improves communication reliability. The findings may be useful for the design and operation of secure communication networks.

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers' workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology's applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

AIR PROTECTION ENCRYPTION PROTOCOLS FOR SECURING AIR TRAFFIC CONTROL COMMUNICATION

The article presents a detailed analysis of the encryption protocols used in air traffic control systems to ensure the security of communication between aircraft and ground systems. The importance of secure communication in air traffic management is highlighted and encryption protocols such as AES, RSA and ECC are examined within the specific context of air traffic control. Special emphasis is placed on secure communication architecture and cryptographic key management solutions. Challenges and solutions in implementing these protocols in complex environments with high security requirements are also discussed.

An IoMT-based Federated Learning Survey in Smart Transportation

Abstract: Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL

Safety and Security-Specific Application of Multiple Drone Sensors at Movement Areas of an Aerodrome

Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.

Harnessing Automation and IIoT in Air Traffic Control for Advanced Efficiency and Safety

The combination of automation and Industrial Internet of Things (IIoT) technology has transformed air traffic control (ATC), improving efficiency and safety in aviation. This article investigates the varied influence of automation and IIoT on ATC operations, emphasizing their importance in maximizing airspace use, boosting situational awareness, and limiting operational hazards. The advent of automatic flight data processing, radar surveillance, and automated decision-support technologies has reduced controller workload and increased operational efficiency in ATC. IIoT sensors and devices enable ATC systems to integrate real-time data on aircraft positions, weather, and runway status, allowing controllers to make more informed decisions and manage air traffic flow. Finally, automation and IIoT have evolved into critical tools for upgrading air traffic control, assisting controllers in managing airspace congestion, optimizing operational performance and safely and quick, efficient traffic movement. This study examines how automation and IIoT alter ATC operations, resulting in a more efficient, robust, and sustainable aviation environment. Keywords— Industrial Internet of Things (IIoT), Air Traffic Control (ATC), Automation, Operational Hazards, Radar surveillance, IoT sensors, Aviation environment.

Speech Recognition for Air Traffic Control Utilizing a Multi-Head State-Space Model and Transfer Learning

In the present study, a novel end-to-end automatic speech recognition (ASR) framework, namely, ResNeXt-Mssm-CTC, has been developed for air traffic control (ATC) systems. This framework is built upon the Multi-Head State-Space Model (Mssm) and incorporates transfer learning techniques. Residual Networks with Cardinality (ResNeXt) employ multi-layered convolutions with residual connections to augment the extraction of intricate feature representations from speech signals. The Mssm is endowed with specialized gating mechanisms, which incorporate parallel heads that acquire knowledge of both local and global temporal dynamics in sequence data. Connectionist temporal classification (CTC) is utilized in the context of sequence labeling, eliminating the requirement for forced alignment and accommodating labels of varying lengths. Moreover, the utilization of transfer learning has been shown to improve performance on the target task by leveraging knowledge acquired from a source task. The experimental results indicate that the model proposed in this study exhibits superior performance compared to other baseline models. Specifically, when pretrained on the Aishell corpus, the model achieves a minimum character error rate (CER) of 7.2% and 8.3%. Furthermore, when applied to the ATC corpus, the CER is reduced to 5.5% and 6.7%.

Robust Trajectory Prediction Using Random Forest Methodology Application to UAS-S4 Ehécatl

Accurate aircraft trajectory prediction is fundamental for enhancing air traffic control systems, ensuring a safe and efficient aviation transportation environment. This research presents a detailed study on the efficacy of the Random Forest (RF) methodology for predicting aircraft trajectories. The study compares the RF approach with two established data-driven models, specifically Long Short-Term Memory (LSTM) and Logistic Regression (LR). The investigation utilizes a significant dataset comprising aircraft trajectory time history data, obtained from a UAS-S4 simulator. Experimental results indicate that within a short-term prediction horizon, the RF methodology surpasses both LSTM and LR in trajectory prediction accuracy and also its robustness to overfitting. The research further fine-tunes the performance of the RF methodology by optimizing various hyperparameters, including the number of estimators, features, depth, split, and leaf. Consequently, these results underscore the viability of the RF methodology as a proven alternative to LSTM and LR models for short-term aircraft trajectory prediction.

Fuzzy decision-making model for solving the ISAO “risk triangle” in conditions of violation of air-craft separation norms.

Taking into account the prevailing influence of the human factor on the occurrence of the vast majority of aviation events and serious incidents, the leading role of such a component of the current ISAO flight safety concept as “the attitude of aviation personnel towards dangerous actions or conditions” has been determined, which well explains the mutual influence of other components of this paradigm. In turn, the components of this relationship are determined through indicators of the influence of the human factor on decision-making by frontline aviation operators, in particular air traffic controllers. This approach is logical, since, firstly, the professional activity of an air traffic controller is easy to imagine as a continu-ous chain of decisions; secondly, according to statistics, unwanted aviation events are the result of false decisions; thirdly, the vast majority of aviation security experts believe that the point is not that “front line” aviation operators do not make decisions at all, but that these decisions are untimely, incor-rect, ineffective, etc. In the context of this publication, such a component of decision making is considered as fuzzy risk as-sessment models, in particular violations of standard operating procedures, namely aircraft separation standards. Using the modifier “very” and the methodology of fuzzy mathematics, a scale (term set) of the linguistic variable “danger level” was formed. The dimension of the scale is larger than that recommended by ISAO, which allows for a more detailed analysis of the dangers of violations of separation standards. On the other hand, scale reduction does not cause difficulties in the conditions of using fuzzy operations of “concentration”, “stretching”, “merging”. Having adapted the well-known “Cooper-Harper scale” for research needs, a survey was conducted of professional air traffic controllers m = 70, employees of the Unified Air Traffic Control System of the Republic of Azerbaijan, who expressed their attitude to the dangers of violations of the separation norms of ACC, ARR aircraft S = 20 km in the form of a “point on the parameter scale”, which contributed to the use of the so-called “hint matrix” for constructing membership functions of the linguistic variable “hazard level” with smooth falling fronts. It has been established that all points of intersection of the membership functions of neighboring terms of the linguistic variable “hazard level” are greater than the “transition point” of L. Zadeh, therefore the distances between them, established on the continuum of the studied separation norm, “rather belong” to the corresponding linguistic assessment of the established scale. The reduction of this scale to the ISAO dimension contributed to the solution of the corresponding “risk triangle”.

Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm.

Method for improving the quality of service of request signals in information systems

The work notes that the request information systems are an important component of information support for air traffic control and airspace control systems. Interrogative information systems include: secondary radar systems, national identification radar systems and short-range navigation radio systems. It is shown that the characteristics of the process of servicing request signals in an aircraft transponder have a significant impact on the statistical properties of the response signal flow, which ultimately, as a consequence, affects the efficiency of the entire radar channel for transmitting response signals with an active response. It is pointed out that one of the important characteristics of the process of servicing request signals in an aircraft transponder is its correlation function, based on the analysis of which one can conclude the choice of the type of response signal flow and the structure of the aircraft transponder. This makes it possible to achieve: increasing the probability of service, increasing the probability of detecting an air object, reducing the root-mean-square error in estimating the azimuth of an air object and increasing the reliability of the transmission of flight information from the observed board of an air object. The article analyzes the possibility of improving the quality of request service by managing the flow of request signals in a network of request information systems. It has been shown that the correct choice of repetition periods of request signals in the area of joint operation of request information systems makes it possible to reduce the likelihood of interaction between individual request signals. Also, as a consequence, it makes it possible to increase the noise immunity of the interrogative information systems under consideration that are part of the specified information system.

A novel intelligent approach for flight delay prediction

Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed.

TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase

Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to go around. Therefore, it is important to accurately predict the flight trajectory of the approach phase. Based on the historical flight trajectories of GA aircraft, a TP model is proposed with deep learning after feature extraction in this study, and the hybrid model combines a time convolution network and an improved transformer model. First, feature extraction of the spatiotemporal dimension is performed on the preprocessed flight data by using TCN; then, the extracted features are executed by adopting the Informer model for TP. The performance of the novel architecture is verified by experiments based on real flight trajectory data. The results show that the proposed TCN-Informer architecture performs better according to various evaluation metrics, which means that the prediction accuracies of the hybrid model are better than those of the typical prediction models widely used today. Moreover, it has been verified that the proposed method can provide valuable suggestions for decision-making regarding whether to go around during the approach.

A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications

Miscommunications between air traffic controllers (ATCOs) and pilots in air traffic control (ATC) may lead to catastrophic aviation accidents. Thanks to advances in speech and language processing, automatic speech recognition (ASR) is an appealing approach to prevent misunderstandings. To allow ATCOs and pilots sufficient time to respond instantly and effectively, the ASR systems for ATC must have both superior recognition performance and low transcription latency. However, most existing ASR works for ATC are primarily concerned with recognition performance while paying little attention to recognition speed, which motivates the research in this article. To address this issue, this article introduces knowledge distillation into the ASR for Mandarin ATC communications to enhance the generalization performance of the light model. Specifically, we propose a simple yet effective lightweight strategy, named Target-Swap Knowledge Distillation (TSKD), which swaps the logit output of the teacher and student models for the target class. It can mitigate the potential overconfidence of the teacher model regarding the target class and enable the student model to concentrate on the distillation of knowledge from non-target classes. Extensive experiments are conducted to demonstrate the effectiveness of the proposed TSKD in homogeneous and heterogeneous architectures. The experimental results reveal that the generated lightweight ASR model achieves a balance between recognition accuracy and transcription latency.

Flight Conflict Resolution and Trajectory Recovery Through Mixed Integer Nonlinear Programming Based on Speed and Heading Angle Change

A scheme for conflict resolution with trajectory recovery is proposed to solve the problem of multi-aircraft flight conflict. First, the conflict resolution problem is modeled as an optimal control problem. The weighted sum of the speed change and the heading angle change is defined as the objective function. The limitations of distance, aircraft performance, and route width are taken as constraints. Second, the optimization problem is resolved by mixed integer nonlinear programming. Conflict resolution with trajectory recovery is then achieved by speed and heading angle changing three times based on the optimal solution; whether the speed and heading angle need to be changed depends on the corresponding weight coefficients in the objective function. Finally, the applicability and superiority of the designed conflict resolution scheme are verified, which is of great significance to the application of conflict resolution with trajectory recovery schemes in automated air traffic control systems.

Assessment of the quality of determining the coordinates of air objects by cooperative radar systems for air surveillance

In the presented work, based on the classification of airspace surveillance radar systems in the form: independent non-cooperative radar surveillance, independent cooperative radar surveillance, dependent cooperative radar surveillance; the quality of determining the coordinates of air objects by the systems under study was assessed. The place and role of these information systems in the information support of airspace control and air traffic control systems is shown. From the calculations carried out, we can draw the following conclusion that the sensitivity of measuring the height of an airborne object significantly depends on the geometry of the location of receiving points of a synchronous network of radar surveillance systems. As the distance between receiving points increases, the area covered by curves of equal sensitivity increases. It is substantiated that when using equal weight in the accuracy of range measurement and in measuring the altitude of an airborne object, the accuracy of the synchronization of the time scales of the receiving points is the value achieved by modern means of time synchronization. The use of the given methodology for assessing the quality of measuring the coordinates of air objects in a synchronous network of cooperative radar surveillance systems for airspace allows us to put forward requirements for the synchronism of time scales in a unified synchronous information network of radar surveillance systems when measuring the coordinates of air objects. It is shown that the price for improving the accuracy of determining the coordinates of air objects by a synchronous network of cooperative radar systems is the complication of the system due to an increase in positions, an increase in the number of transceiver paths, the need to synchronize emission processes, receive signals and control viewing modes.

Air Traffic Sector Network: Motif Identification and Resilience Evaluation Based on Subgraphs

Air traffic control systems play a critical role in ensuring the sustainable and resilient flow of air traffic. The air traffic sector serves as a fundamental topological unit and is responsible for overseeing and maintaining the system’s sustainable operation. Examining the structural characteristics of the air traffic sector network is a useful approach to gaining an intuitive understanding of the system’s sustainability and resilience. In this paper, an air traffic sector network (ATSN) was established in mainland China using the complex network theory, and its motif characteristics were analyzed from a microscopic perspective. Additionally, subgraph resilience was defined in order to describe the network topology by analyzing changes in subgraph motif concentration and subgraph residual concentration. Our empirical findings indicated that motifs exhibit high connectivity, while anti-motifs are found in subgraph structures with low connectivity. The motif concentration of subgraphs can efficiently reflect the distribution of heterogeneous subgraph structures within a network. During the process of resilience evaluation, the subgraph motif concentration remains relatively stable but is sensitive to the transition state of the network from disturbance to recovery. The resilience of the system at the macroscopic scale is aligned with the resilience of each heterogeneous subgraph structure to some extent. Topological indicators have a more significant impact on the resilience of the ATSN than air traffic flow characteristics. This study has the outcome of uncovering the preference for connection among nodes and the rationality of sector structure delineation in ATSNs. Additionally, this research addresses the fundamental mechanism behind the network disturbance recovery process, and identifies the connection between network macro- and microstructure in the resilience process.

Challenges to the Operational Safety and Security of eVTOL Aircraft in Metropolitan Regions: A Literature Review

The start of urban air mobility operations using helicopters began in 1940s in Los Angeles to transport passengers and mail between various locations. However, the concept of urban air mobility (UAM) only emerged in the 1960s. Its implementation used helicopters to avoid congestion by land, typical of populated metropolitan regions. However, there was a severe limitation in using these aircraft because of accidents, huge operating costs, and high noise generation levels. In 2010, a new UAM concept conceived electric vertical take-off and landing (eVTOL) vehicles. These vehicles, in principle, would be safer, with lower operating costs and less noisy than helicopters. Several aircraft manufacturers have developed dozens of eVTOL models, focusing on the feasibility of using them for UAM in some metropolitan regions. However, we must consider several challenges to materialize this new concept, including those related to operational safety and security. Among these challenges is these vehicle operations integration to the airspace structure and the air traffic control system, the new embedded and ground systems development, the ground infrastructure implementation to support the operations, the reliability of air navigation systems, the meteorological information provision in real-time and with complete coverage of metropolitan airspaces to be used by eVTOL, and others. Despite some similarities, this new UAM concept will not be identical for all urban regions due to differences related to topographies, weather, urban construction patterns and air traffic control systems. Therefore, local studies are essential to support gradual and safe implementations of operations with these vehicles. This paper presents a literature review, identifying and discussing the utmost safety and security-related challenges researchers observe.