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@article{197732,
        author = {Dr. C. Kavitha and Sanjai K and Subiksha S and Sugan R and Vimal M},
        title = {Radar Signal Processing for UAV Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7204-7208},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197732},
        abstract = {The increasing use of small Unmanned Aerial Vehicles (UAVs) in surveillance and tactical operations has created new challenges for airspace security. Detecting these UAVs using conventional radar systems is difficult because such systems are mainly designed for large aircraft with high radar cross sections (RCS). In contrast, small UAVs are often built using lightweight composite materials, which produce weak radar reflections and make reliable detection challenging. This paper presents a MATLAB-based pulse radar signal-processing framework to improve UAV detection in noisy environments. The system generates a rectangular pulse signal, models the UAV echo as a delayed and attenuated return signal, and introduces noise using an Additive White Gaussian Noise (AWGN) model. A matched filter is then applied to enhance the signal-to-noise ratio (SNR), followed by peak detection to determine the time delay of the received signal. Using this delay, the range of the UAV is estimated. Simulation results show that matched filtering significantly improves detection performance and helps reduce range estimation errors even under low SNR conditions. In the simulation, the UAV is detected at an approximate range of 1500 m with minimal error. These results demonstrate that the proposed approach provides an effective and reliable basis for radar-based UAV detection and monitoring systems.},
        keywords = {UAV detection, pulse radar, matched filter, radar signal processing, SNR, range estimation, AWGN, MATLAB simulation.},
        month = {April},
        }