Geospatial Guidance of Unmanned Aerial Vehicles around no-fly-zones by Global Positioning System Spoofing

Ortner, Philipp; Steinhöfler, Raphael; Hodl, Patrick; Leitgeb, Erich; Flühr, Holger

doi:10.1109/isncc52172.2021.9615885

Cited by 2 publications

(2 citation statements)

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“…To generate a user-defined number of flights with a selection of pseudo-randomly weighted air traffic conflicts, individual flight models with different flight profiles are used [15][16][17]. Each simulated flight receives an individual CS and 24-bit Mode S address.…”

Section: Generation and Processing Of Air Traffic Datasets With Air T...mentioning

confidence: 99%

“…In this context, a user-defined number of flights with a selection of pseudo-randomly weighted air traffic conflicts are generated. The datasets exposed to the LSTM networks contain realistic air traffic models with a corresponding percentage of manipulated flights [16,17]. Regarding weather events, the dataset contains 34.26% manipulated flights.…”

Section: Introductionmentioning

confidence: 99%

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Augmented Air Traffic Control System—Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts

Ortner

Steinhöfler

Leitgeb

et al. 2022

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Today’s air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deterioration of flight safety, greater costs, more delays, and higher emissions. Transforming the ATM into the “next generation” requires complex human-integrated systems that provide better abstraction of airspace and create situational awareness, as described in the literature for this problem. This paper makes the following contributions: (a) It outlines the complexity of the problem. (b) It introduces a digital assistance system to detect conflicts in air traffic by systematically analyzing aircraft surveillance data to provide air traffic controllers with better situational awareness. For this purpose, long short-term memory (LSTMs) networks, which are a popular version of recurrent neural networks (RNNs) are used to determine whether their temporal dynamic behavior is capable of reliably monitoring air traffic and classifying error patterns. (c) Large-scale, realistic air traffic models with several thousand flights containing air traffic conflicts are used to create a parameterized airspace abstraction to train several variations of LSTM networks. The applied networks are based on a 20–10–1 architecture while using leaky ReLU and sigmoid activation function. For the learning process, the binary cross-entropy loss function and the adaptive moment estimation (ADAM) optimizer are applied with different learning rates and batch sizes over ten epochs. (d) Numerical results and achievements by using LSTM networks to predict various weather events, cyberattacks, emergency situations and human factors are presented.

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Section: Generation and Processing Of Air Traffic Datasets With Air T...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%