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@article{185976,
        author = {Dr Shishir mishra},
        title = {AI-Enabled Radar for Drone Operations: Detection, Tracking, and Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {4011-4014},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185976},
        abstract = {This paper presents a practical framework for integrating radar signal processing with lightweight machine learning to enable robust UAV perception for collision avoidance, target classification, and autonomous navigation. We review core signal-processing components (FMCW/IQ acquisition, FFT-based range mapping, Doppler processing), adaptive detection using CFAR, micro-Doppler feature extraction, and edge deployment of compact neural networks (TensorFlow Lite / TinyML). A reference pipeline is described alongside implementation strategies for embedded platforms and evaluation methodology using simulated and micro-Doppler datasets. Results indicate that combining classical radar detection with small CNN/ML classifiers provides reliable drone vs. bird discrimination and improves situational awareness in degraded-visibility conditions.},
        keywords = {},
        month = {November},
        }