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@article{192331,
        author = {Sushrut Deshmukh and Vidya Waghchoure and Mayur Gore and Aditya Mokase and Akash Palde},
        title = {SpaceDebrisRadar.AI: ML-Based System for Anomaly Detection and Traffic Forecasting in LEO},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1464-1469},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192331},
        abstract = {Since the launch of the first man-made satellite, Sputnik 1 by the Soviet Union (USSR), the space race has begun across the globe, in which the USA is currently in the lead. Due to this advancement in the space race, the Earth has become heavily crowded by satellites. Not just complete satellites, but the debris generated through the collision of the satellites is increasing day by day. If this continues, a phenomenon called ‘Kessler Syndrome' can occur, in which collision of satellites can create a chain reaction and contribute to even more debris. There is a clear need to address this problem. To tackle this challenge, we aim to provide an easy yet effective solution, which is not reactive, but proactive through SpaceDebrisRadar.AI. The objective of this system is to identify potentially risky zones in the Earth’s orbit, so that newly launched satellites can operate more securely and contribute to safer and more sustainable use of space.},
        keywords = {Anomaly Detection, Kessler Syndrome, Low Earth Orbit (LEO), Machine Learning, Space Debris.},
        month = {February},
        }