Integrated Display and Simulation for Automatic Dependent Surveillance–Broadcast and Traffic Collision Avoidance System Data Fusion

Wang, Yanran; Xiao, Gang; Dai, Zhouyun

doi:10.3390/s17112611

Cited by 6 publications

(4 citation statements)

References 15 publications

(13 reference statements)

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“…As a new generation of air traffic management surveillance technology, ADS-B can broadcast aircraft position, velocity, heading and other flight information in real-time which improves the visibility of air traffic, reduces flight conflicts and enhances the efficiency and safety of ATM [ 1 ]. However, since ADS-B data are broadcast in plaintext format and lack an authentication mechanism, the attackers can easily jam, eavesdrop, modify, inject and delete ADS-B messages [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management

Luo,

Wang,

Tian

et al. 2024

Sensors

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Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).

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Section: Introductionmentioning

confidence: 99%

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management

Luo,

Wang,

Tian

et al. 2024

Sensors

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“…Standard ACAS (Airborne Collision Avoidance System) class anticollision systems [6], such as TCAS (Traffic Alert and Collision Avoidance System) [7], require the use of SSR (Secondary Surveillance Radar) transponders and directional antenna systems [8]. TCAS is an airborne traffic alarm and collision avoidance system which is independent of air traffic control on the ground [9]. TCAS is not always applicable for small (and even more so ultralight) aircraft, mainly due to their mass and dimensions, as well as high installation costs.…”

Section: Introductionmentioning

confidence: 99%

“…ADS-B is more affordable and can be used for both auxiliary traffic collision avoidance and surveillance in the airspace system. The possibilities of data fusion from TCAS and ADS-B systems are also very promising [9,10]. Due to the costs of purchase, installation and maintenance, but also due to the specificity of the intruder detection and avoidance algorithms required in small aircraft [11], sailplanes, and RPAS/UAV (unmanned aerial vehicles) [12,13], other systems have also been developed.…”

Section: Introductionmentioning

confidence: 99%

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In-Flight Tests of Intruder Detection Vision System

Rzucidło

Jaromi

Kapuściński

et al. 2021

Sensors

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In the near future, the integration of manned and unmanned aerial vehicles into the common airspace will proceed. The changes taking place mean that the safety of light aircraft, ultralight aircraft and unmanned air vehicles (UAV) will become an increasing problem. The IDAAS project (Intruder Detection And collision Avoidance System) meets the new challenges as it aims to produce technically advanced detection and collision avoidance systems for light and unmanned aerial vehicles. The work discusses selected elements of research and practical tests of the intruder detection vision system, which is part the of IDAAS project. At the outset, the current formal requirements related to the necessity of installing anticollision systems on aircraft are presented. The concept of the IDAAS system and the structure of algorithms related to image processing are also discussed. The main part of the work presents the methodology developed for the needs of dedicated flight tests, its implementation and the results obtained. The initial tests of the IDAAS system carried out on an ultralight aircraft generally indicate the possibility of the effective detection of intruders in the airspace with the use of vision methods, although they also indicated the existence of conditions in which this detection may prove difficult or even impossible.

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Research on safety modeling and analysis in information fusion system

Xiao

Wang

2018

Self Cite

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Avionics system integration is a prominent trend in research and development of civil airplane. It can improve task effectiveness, function efficiency, and resource utilization of system. Information fusion, which includes function information fusion, processing information fusion, and sensor input fusion, is a core process of avionics system integration. Some researches about the impact of information fusion on system safety are done in avionics system which is shown as follows: (1) the concept of Mishap Dilution, Mishap Implication and Mishap Confusion (MD-MI-MC) is first defined in function information fusion of avionics system. (2) The model of multi-source MD-MI-MC is established based on hazard theory. (3) The function fusion of Automatic-Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) is used as a typical example to analyze fusion system during the aircraft climbing or landing state. In this paper, the concept and model of multi-source MD-MI-MC are proposed for safety analysis of integrated avionics system. A fusion model with a variable sampling Variational Bayesian-Interacting Multiple Model (VSVB-IMM) algorithm is used to analyze. At last, a set of theory system and evaluation standards including the positive and negative earning analyses are built based on the presented MD-MI-MC theory and mechanism of integrated avionics.

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Integrated Display and Simulation for Automatic Dependent Surveillance–Broadcast and Traffic Collision Avoidance System Data Fusion

Cited by 6 publications

References 15 publications

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management

In-Flight Tests of Intruder Detection Vision System

Research on safety modeling and analysis in information fusion system

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