Work

Accurate prediction approach for the center position of a future light spot under atmospheric turbulence

Year: 2025

Type: article

Abstract: Atmospheric turbulence significantly impacts satellite-to-ground laser communication (SGLC). To enhance the accuracy and stability of the pointing, acquisition, and tracking (PAT) system, we propose a hybrid method for predicting the center position of light spots, named feature matching recurrent prediction (FMRP). The foundational architecture of FMRP is based on a recurrent neural network (RNN) structure, with feature matching utilized to optimize the acquisition of input features and labels. Unlike most previous deep learning-based methods, a key advantage of FMRP is that it can achieve accurate position prediction only by processing real-time received spot images, eliminating the need for offline training, and it does not require the collection of a large dataset for model training. Experimental validation demonstrates that this method is effective under both weak and strong atmospheric turbulence, exhibiting high prediction accuracy and stability.

Source: Optics Express

DOI: 10.1364/oe.568684

Authors: Xuewei Wang, Ninghua Zhang, Qiang Wang, Lei Cui

Language: English

Topic: Optical Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: National Natural Science Foundation of China, Chinese Aeronautical Establishment

Awards: G1306702661, G3207104983, G7236357095, G7683078858