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<p>Precision Navigation and Timing systems, integral to air traffic management, primarily rely on the Global Navigation Satellite System (GNSS) for accurate data transmission. However, GNSS signals, particularly those used in Automatic Dependent Surveillance-Broadcast (ADS-B) systems, are susceptible to interference, leading to potential safety risks in aviation. This study presents a novel AI-driven Flight Trajectory Prediction Framework, leveraging machine learning and deep learning techniques to detect and mitigate GNSS signal interference. By training models on ADS-B data, this framework identifies potential interference and evaluates its impact on air traffic. The implementation of this AI-based system enhances the reliability and security of air navigation, significantly reducing human error and elevating overall safety standards in aviation. Experimental results demonstrate the framework’s efficacy in improving navigational accuracy and operational efficiency within modern air traffic control systems.</p>

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Enhancing aviation safety and efficiency: applying artificial intelligence (AI) to address navigational challenges

  • 期刊论文

  • 发表日期：

    2025年8月12日

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  • Alyaa A. Hamza,
  • Rehan Ahmed Yosef,
  • Kamel Hussien Rahouma

摘要

Precision Navigation and Timing systems, integral to air traffic management, primarily rely on the Global Navigation Satellite System (GNSS) for accurate data transmission. However, GNSS signals, particularly those used in Automatic Dependent Surveillance-Broadcast (ADS-B) systems, are susceptible to interference, leading to potential safety risks in aviation. This study presents a novel AI-driven Flight Trajectory Prediction Framework, leveraging machine learning and deep learning techniques to detect and mitigate GNSS signal interference. By training models on ADS-B data, this framework identifies potential interference and evaluates its impact on air traffic. The implementation of this AI-based system enhances the reliability and security of air navigation, significantly reducing human error and elevating overall safety standards in aviation. Experimental results demonstrate the framework’s efficacy in improving navigational accuracy and operational efficiency within modern air traffic control systems.