Created on 9th November 2024
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SkyGuard: Flight Path Anomaly Detection System addresses the critical need for real-time monitoring and anomaly detection in commercial flight paths to enhance aviation safety. With the ever-increasing number of flights and complex air traffic, identifying deviations from expected flight patterns is crucial to preventing accidents and ensuring efficient airspace management. Using advanced LSTM-based and Isolation forest models, SkyGuard analyzes flight parameters such as azimuth, speed, and heading to detect irregular behaviors that could indicate safety hazards, system failures, or unauthorized deviations. Beyond commercial safety, the system can also identify enemy aircraft deviating from normal flight paths, providing an additional layer of security and early threat detection. By delivering timely alerts, SkyGuard empowers aviation authorities and airlines to take proactive measures, mitigating risks and safeguarding the skies.
During the development of SkyGuard, I encountered several significant challenges. One of the primary obstacles was obtaining a comprehensive and reliable dataset, as detailed flight path records are not readily available in the public domain. The restricted access to such crucial data made sourcing sufficient information for training and testing the anomaly detection model particularly difficult. Additionally, the data processing phase posed considerable challenges due to the substantial amount of noise present in the dataset. Inconsistencies, missing values, and outliers required extensive cleaning and pre-processing to ensure the model could learn effectively and produce accurate results. Overcoming these hurdles demanded innovative solutions and a strong emphasis on data quality to make the system reliable and effective for real-world applications.
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