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

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