Enhancing target recognition rate in atmospheric turbulence using orbital angular momentum spectra of vortex beams

• DOI: 10.1088/1402-4896/ad8af9
• Publication Date: 2024-10-24T22:53:44Z
• Authors: Chengshuai Cui; Hongyang Wang; Zijing Zhang; Runzi Lin; Hao Yun; Xinran Lv; Jiayu Song; Rui Feng; Yuan Zhao
• Keywords: Momentum (technical analysis)

Abstract Traditional methods for extracting and recognizing targets from laser echo signals typically involve complex processing require extensive data. Vortex beams carry orbital angular momentum (OAM), upon reflection a target, the distribution of OAM spectrum carries features related to thereby enriching dimensions target recognition. Using simplifies recognition process but faces challenges like atmospheric turbulence that affect beam transmission accuracy. Our study employs Gerchberg–Saxton phase retrieval (GS) algorithm mitigate effects on beams. data, we achieved effective with various shapes under through back-propagation neural network (BPNN). Simulations revealed rate increase 76.25% 96% post-compensation by GS algorithm. We also found highest occurs at ratio 0.2. After compensation 0.1, each shape increased 99%. This demonstrates effectiveness utilizing diverse shapes, further improving rates. These findings can be applied intelligent transportation robotic vision.