Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

Intelligent Virtual Assistant System(IVAS) for Air Traffic Controllers

Air traffic control is an essential obligation in the aviation industry to have safe and efficient air transportation.Year by year,the workload and on-job-stress of the air traffic controllers are rapidly increasing due to the rapid growth of air traveling.Controllers are usually dealing with multiple aircrafts at a time and must make quick and accurate decisions to ensure the safety of aircrafts.Heavy workload and high responsibilities create air traffic control a stressful job that sometimes could be error-prone and time-consuming,since controlling and decision-making are solely dependent on human intelligence.To provide effective solutions for the mentioned on the job challenges of the controllers,this study proposed an intelligent virtual assistant system(IVAS)to assist the controllers thereby to reduce the controllers’workload.Consisting of four main parts,which are voice recognition,display conversation on screen,task execution,and text to speech,the proposed system is developed with the aid of artificial intelligence(AI)techniques to make speedy decisions and be free of human interventions.IVAS is a computer-based system that can be activated by the voice of the air traffic controller and then appropriately assist to control the flight.IVAS identifies the words spoken by the controller and then a virtual assistant navigates to collect the data requested from the controllers,which allows additional or free time to the controllers to contemplate more on the work or could assist to another aircraft.The Google speech application programming interface(API)converts audio to text to recognize keywords.AI agent is trained using the Hidden marko model(HMM)algorithm such that it could learn the characteristics of the distinct voices of the controllers.At this stage,the proposed IVAS can be used to provide training for novice air traffic controllers effectively.The system is to be developed as a real-time system which could be used at the air traffic controlling base for actual traffic controlling purposes and the system is to be further upgraded to perform the task by recognizing keywords directly from the pilot voice command.

Identification of Information-Seeking Behaviors from Air Traffic Controllers′ Eye Movements

Air traffic controllers are the important parts of air traffic management system who are responsible for the safety and efficiency of the system.They make traffic management decisions based on information acquired from various sources.The understanding of their information seeking behaviors is still limited.We aim to identify controllers′ behavior through the examination of the correlations between controllers′eye movements and air traffic.Sixteen air traffic controllers were invited to participate real-time simulation experiments,during which the data of their eye ball movements and air traffic were recorded.Tweny-three air traffic complexity metrics and six eye movements metrics were calculated to examine their relationships.Two correlational methods,Pearson′s correlation and Spearman′s correlation,were tested between every eye-traffic pair of metrics.The results indicate that controllers′two kinds of information-seeking behaviors can be identified from their eye movements:Targets tracking,and confliction recognition.The study on controllers′ eye movements may contribute to the understanding of information-seeking mechanisms leading to the development of more intelligent automations in the future.

Effect of Working Experience on Air Traffic Controller Eye Movement

Eye movement is an important indicator of information-seeking behavior and provides insight into cognitive strategies which are vital for decision-making.Various measures based on eye movements have been proposed to capture humans’ability to process information in a complex environment.The effectiveness of these measures has not yet been fully explored in the field of air traffic management.This paper presents a comparative study on eye-movement measures in air traffic controllers with different levels of working experience.Two commonly investigated oculomotor behaviors,fixation and saccades,together with gaze entropy,are examined.By comparing the statistical properties of the relevant metrics,it is shown that working experience has a notable effect on eye-movement patterns.Both fixation and saccades differ between qualified and novice controllers,with the former type of controller employing more efficient searching strategies.These findings are useful in enhancing the quality of controller training and contributing to an understanding of the information-seeking mechanisms humans use when executing complex tasks.

Performance Evaluation of Terminal Airspace System Safety, Delay and Predictability of Muritala Mohammed International Airport, Ikeja Lagos, Nigeria

This paper outlines a multi-dimensional user-oriented performance metrics approach in evaluating the operation of the terminal airspace system to aid in the airport and airspace planning and decision making. Safety, delay and predictability metrics contribute to the analytical framework. From the findings, the occurrence of air incidence has a high severity level at departure, and arrival phases of flight, higher likelihood at the radar room and much of the incidences were as a result of faulty equipment and inherent absence of modern airspace infrastructure. Also, in Lagos terminal airspace, the number of incidences has no close correlation with the level of traffic complexity. Total schedule arrival delay ranges from 1 - 392 minutes representing an average of 7.8 - 17.9 minutes per aircraft that arrived Lagos airport at that period. Besides, the total approach contact time ranges from 1 - 57 minutes, translating to 4.6 - 7.1 minutes per aircraft. However, variability in arrival time of 1 - 5 minutes is common from published airline arrival scheduled time. In the same vein, the variability of 1 - 5 minutes is common from approach contact times of aircraft. These figures indicate sound arrival predictability signature for Lagos airport. Also, departure time variability above 30 minutes is familiar from the ATC clearance time for the various routes under study. However, there is about or more 25% variability of more than 15 minutes, and this indicates possible inconsistency of predicting departure times from the times Air Traffic Control (ATC) clearance was acquired. Above all, the predictability of departure times in Lagos airport is weak compared to those of the arrival. Taken by it, this may be a sign of airspace congestion or ATC deficiencies at the Lagos airport. This is an indication of the lack of users’ confidence in Nigeria’s air transport industry to deliver just-in-time service.

基于航迹预测的复杂点融合系统航班排序研究

为了提高复杂点融合系统的运行效率,对复杂点融合系统的航班排序问题进行了研究。通过BADA性能数据库对航班进行航迹预测,充分考虑了系统外的冲突对点融合系统排序的影响。以进场航班的点融合系统内飞行时间最小为目标,以安全运行间隔为约束条件,使用改进蝴蝶优化算法进行优化,并使用TAAM仿真软件进行仿真验证。经大量仿真,整体优化率在30%左右波动,证明了排序优化模型的鲁棒性和适用性,为点融合系统排序优化方面的研究提供了新思路。

中国省会航空节点城市复杂时空系统协调度变化及空间特征分析

航空运输业提升了城市系统的吸引力并激发新的交通需求,引导城市经济和空间结构快速演进。本文构建了包含经济、规模、外贸、科技、社会、环境、航空等要素在内的城市复杂时空系统指标体系,对中国省会航空节点城市及其构成的7个空管区的复杂时空系统发展的协调程度变化情况及时空聚类特征、空间溢出效应进行了分析。研究发现:2001—2019年,中国31个省会(首府、直辖市)航空节点城市在复杂时空系统发展过程中呈现了或分散、或集中的趋势特征,南方城市在研究末期的协调程度表现更好。7个空管区中,华北区协调度最高,新疆区、西北区、华东区、中南区的协调度水平发展增速较快,航空系统的平均协调度最高。各航空节点城市复杂时空系统协调度的时空聚类模式存在空间分异特征与聚类特征,航空系统在空管区的聚类优势最为突出,经济系统在空管区聚类辐射范围最大。航空、规模、科技与环境系统均表现了正向直接空间溢出效应,经济与科技系统表现了正向间接空间溢出效应。通过全面分析航空节点城市及空管区的复杂时空系统协调度变化,精准判定了航空节点城市多指标系统在区域发展过程中的影响程度及时空分布异质性,对拓展航空地理研究及促进航空节点城市高质量综合发展提供理论依据。

STPA和CREAM方法在飞行冲突调配人因差错研究中的应用

为了研究管制员飞行冲突调配的人因差错问题,进而有效评估管制员解决飞行冲突的可靠性,以保障空中交通的安全运行,提出系统理论过程分析(System Theoretic Process Analysis, STPA)与认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM)相结合的人因可靠性分析方法。首先,通过STPA方法构建系统控制模型,识别不安全控制行为(Unsafe Control Action, UCA)以及致因因素,找到管制员在调配飞行冲突过程中可能存在的差错行为;其次,基于CREAM扩展法对管制员的差错行为进行定量分析,得到管制员调配飞行冲突的人因失误概率。研究显示:使用该方法能够系统、全面地识别出管制员在调配飞行冲突过程中出现的差错行为,进而计算管制员飞行冲突调配的人因失误概率。实例分析表明该方法可以预测管制员在飞行冲突调配过程中的人因失误概率及可靠性,为管制员人因可靠性分析提供了新思路。

接入ADS-B对航空管制自动化系统的影响研究

航空管制(以下简称"空管")是保证航运安全的关键.随着航空管制的升级,出现了一些新型技术,如ADS-B技术.将ADS-B技术接入到空管自动化系统中以后,航空的监管技术得到明显提升,并且可为管制人员提供实时数据和信息,有助于航空管制工作的开展.阐述了ADS-B技术以及空管自动化系统和ADS-B接入存在的风险,提出了ADS-B技术在空管自动化系统的应用优势.

THEMODELINGOFATCSYSTEMSBASEDONPETRINET

提出了一种用面向对象Petri网为空中交通管制系统建模的方法，给出了这种高级Petri网的形式化定义。

AirNet空管自动化系统航迹数据处理故障浅析

AirNet空管自动化系统采用动态加权方法融合多路监视源数据,计算出接近于真实情况的航迹数据。随着空中交通流量的飞速上升和监视源数据质量的变化,AirNet空管自动化系统在运行过程中遇到了新的问题。基于此,笔者针对高度跳变、速度跳变和特殊代码误告警3个故障,分析其原因并提出合理的改进方法。

关于AirNet空管自动化系统监视数据处理的研究

AirNet空管自动化系统由中国民航第二研究所研发,是一套基于Linux多任务操作系统的空管自动化系统。该系统主要对监视数据和飞行计划数据进行处理,为管制员提供航空器在空中的飞行动态信息、安全间隔信息和相关告警信息。AirNet空管自动化系统是管制员进行空中交通管制的重要工具,是空管运行部门主要的空中监视手段。本文主要对AirNet空管自动化系统监视数据处理功能进行研究,以便更好地掌握和使用该系统。

空中交通管制员选拔系统的有效性研究

空中交通管制员(以下简称管制员)负责实时指挥航空器的空中以及地面交通活动。该岗位对于管制员的空间想象力、短时记忆能力、情景意识等能力均有着很高的要求。为了选拔出综合素质优秀的管制员,设计了初始管制员选拔系统,之后通过测试成绩对该系统进行了信度与效度检验,在分析系统有效性的同时对系统的测试效果进行了验证。应用该系统对1675名管制学员进行测试,采集总分和各分项目的成绩数据,使用Mann-Whitney U检验方法分析了受试者男性和女性在总分和各分项目上的得分差异,采用Cronbach’sα系数和Spearman-Brown系数评价了系统的信度,根据专家评定结果对其内容效度进行检验,对所获数据进行探索性因子分析从而验证系统的结构效度,根据受试者工作特征曲线(receiver operating characteristic curve,ROC)得出各分项目的划界分,并计算各分项目的难度系数。结果表明:男女之间在部分项目上存在统计学差异;各分项目区分度良好;信度和效度检验符合测量学要求;根据ROC曲线得出的各分项目难度系数和划界分能够为管制员选拔提供参考;系统在管制学员的能力测试方面有很好的效果。

基于云模型的空管运行系统脆弱性评估

为了评估空管运行系统脆弱性,首先,对2011—2018年这8年中1 018起空管不安全事件进行分析,将致因归类后从中提取空管运行系统脆弱性影响因素,按照空管系统管制员、设备、环境、管理4个方面,以及运行环境中飞行机组、机场保障、航空器、签派和军航活动等5个部分,共归纳出28个空管运行系统脆弱性评估指标。其次,通过层次分析和熵权法求得综合权重;考虑脆弱性影响因素,采用具有模糊性和随机性特点的云模型对系统扰动的特性进行评估;采用Delphi法打分后,利用正向云发生器绘制出能直观反映各个指标脆弱性等级的云图。最后,将评估结果与模糊综合评价法进行对比分析,结果表明,民航华北空中交通管理局空管运行系统脆弱性等级总体趋于“较高”水平,不安全事件频发,其中军航活动和管制员因素影响最大。

Automatic interval management for aircraft based on dynamic fuzzy speed control considering uncertainty

A novel real-time autonomous Interval Management System(IMS)is proposed to automate interval management,which considers the effect of wind uncertainty using the Dynamic Fuzzy Velocity Decision(DFVD)algorithm.The membership function can be generated dynamically based on the True Air Speed(TAS)limitation changes in real time and the interval criterion of the adjacent aircraft,and combined with human cognition to formulate fuzzy rules for speed adjusting decision-making.Three groups of experiments were conducted during the en-route descent stage to validate the proposed IMS and DFVD performances,and to analyze the impact factors of the algorithm.The verification experimental results show that compared with actual flight status data under controllers’command,the IMS reduces the descent time by approaching 30%with favorable wind uncertainty suppression performance.Sensitivity analysis shows that the ability improvement of DFVD is mainly affected by the boundary value of the membership function.Additionally,the dynamic generation of the velocity membership function has greater advantages than the static method in terms of safety and stability.Through the analysis of influencing factors,we found that the interval criterion and aircraft category have no significant effect on the capability of IMS.In a higher initial altitude scenario,the initial interval should be appropriately increased to enhance safety and efficiency during the descent process.This prototype system could evolve into a realtime Flight-deck Interval Management(FIM)tool in the future.

空管雷达威力数字化分析

针对空管雷达网任务筹划系统雷达威力图数据密度低、精度差、负仰角数据缺失和人工读图效率低等问题,提出了一种基于图像处理的空管雷达威力数字化分析方法,自动计算威力图像素点位置和雷达威力图坐标对应关系,补全缺失数据;给出了威力图量化处理流程和实际威力计算方法,进而达到自动分析实际雷达探测性能的目的.实际处理结果验证了该方法的有效性.

空管自动化系统、塔台管制自动化系统安全计算环境下的补丁管理分析

经过对当前民航规章要求以及自动化系统的现状分析,提出自动化系统的操作系统环境面临的问题和环境补丁的管理现状;并参考其他行业系统环境补丁管理的方式方法,给出自动化系统环境补丁的管理方法,希望能够为空管自动化系统的维护者提供参考。

CNAS实验室认可体系在空管自动化系统软件测试中的应用

自动化系统软件质量保障在民航安全运行中发挥重要作用,软件测试是保证软件质量的重要手段,文章分析了目前空管自动化系统软件测试中的问题,研究实验室认可体系的要求在空管自动化系统软件测试中的应用,以达到提高测试水平,进而保证自动化系统软件质量的目的。