Cost-Efficient and Effective Counter Unmanned Aerial System via Visual-Acoustic Sensing

As Unmanned Aerial Vehicles (UAVs) become more accessible to the public, they become a common tool for malicious purposes. As a result, there is an increasing demand for Counter Unmanned Aerial Systems (CUAS) that can detect UAVs. Existing CUAS solutions often rely on high-priced radar systems or advanced technologies, primarily designed for military purposes. In this paper, a low-cost, effective, non-military CUAS that uses inexpensive smartphones' microphones and camera, along with machine learning models, is proposed to detect and track a malicious UAV (MUAV) in real-time. Our proposed CUAS is designed to be affordable and accessible to the general public, operating automatically to detect and track MUAVs in real-time.