Revolutionizing Public Safety: Acoustic Vector Sensor for UAV Detection

Introducing a groundbreaking acoustic vector sensor system designed to detect and localize unmanned aerial vehicles (UAVs) in real-time, developed by the esteemed Prof. Kapil Tyagi. This innovative project combines advanced signal processing techniques, deep learning algorithms, and novel sensor integration to provide unparalleled accuracy and reliability.
Key features and achievements:
Convolutional Recurrent Neural Network (CRNN): Achieves a remarkable 95% accuracy in UAV detection, even in complex environments.
Novel Localization Method: Utilizes phase differences from multiple sensors for precise source location estimation.
Enhanced Dataset Quality: Employs preprocessing techniques like bandpass and adaptive filtering to improve detection performance.
Additional Sensor Integration: Incorporates sensors for azimuth, elevation, and range estimation to provide a comprehensive solution.
Spiking Deconvolution Algorithm: Improves the accuracy of time delay estimation between acoustic signals.
Discover how this cutting-edge technology, developed under the guidance of Prof. Kapil Tyagi, is revolutionizing public safety and security by safeguarding against unseen threats. Join us as we explore the future of UAV detection and localization.
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