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@article{184709,
        author = {Prof. Ramgopal Sahu and Jay M. Nale and Kaushal N. Patil and Samarath A. Anandkar and Rugved S. Dhable and Snehal S. Parashare},
        title = {LRF based Anti-Drone Detection and Destruction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3228-3236},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184709},
        abstract = {Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become increasingly valuable in areas such as surveillance, agriculture, and logistics. However, their rising popularity has also introduced new security concerns, especially when drones are used without authorization in sensitive or restricted zones. To address these challenges, this system proposes an intelligent anti-drone system capable of detecting, tracking, and neutralizing unauthorized drones in real-time. The system leverages deep learning techniques, specifically Deep Convolutional Neural Networks (D-CNN), to accurately identify drones from visual input. Once detected, the system uses trajectory prediction algorithms to monitor drone movements with high precision. To neutralize potential threats, a laser-based hard kill mechanism could be employed, providing a precise and controlled method of intervention without collateral damage. The integration of these technologies aims to enhance security in critical areas by offering a fast, accurate, and effective response to rogue drones. This solution not only addresses current security needs but also sets a foundation for future advancements in automated drone countermeasures.},
        keywords = {},
        month = {September},
        }