A Compressive Sensing Control Matrix Generating Technique to Extend the Field of View for Drone Detection Optical Systems - Thao, Hoang Thi Phuong; Cuong, Nguyen Le; Kien, Tran Vu (Engineering, Technology & Applied Science Research, 2025)
Detecting drones based on optical images is a popular topic in current research. However, most current optical systems have a narrow Field of View (FoV), so they often combine many cameras into scanning systems to observe a wide area, reducing object detection speed. This paper presents an optical model that applies compressive sensing techniques to capture images from two FoVs using a single 2D image sensor. This work aims to enhance drone detection by improving image capture efficiency, reducing the need for multiple cameras, and improving detection speed. The sampling process utilizes a Digital Mirror Device (DMD) combined with the proposed control technique. The control technique uses a matrix with columns formed from pseudorandom binary sequences generated from primitive polynomials over a finite field through trace functions and D-transforms. After compressed sensing, data recovery is performed using the popular Orthogonal Matching Pursuit (OMP) recovery algorithm. The system can reduce the number of cameras required and increase object detection speed by not using a scanner such as traditional systems. The effectiveness of the proposed model is evaluated based on Peak Signal Noise Ratio (PSNR), Normalized Root Mean Squared Error (NRMSE), and processing time, using simulated data of drone images under various conditions. The simulation results demonstrate the system's effectiveness, with PSNR ranging from 24.52 to 52.39 dB, NRMSE between 0.004 and 0.081, and reduced processing times, validating its feasibility and advantages for real-time wide-FoV optical drone detection.