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基于深度学习的水下激光通信系统弱光信号检测方法研究

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水下激光通信系统弱光信号受到频谱和多径干扰,检测的精度不高,为了提高水下激光通信的稳定性,提出基于深度学习的水下激光通信系统弱光信号检测方法。采用空间波束形成方法构建水下激光通信采集和多层频谱分解模型,实现信号的波束特征增强处理,结合时间反转技术估计弱光信号的射频标签分布信息和多径信息,采用多径衰落抑制实现对水下激光通信系统的弱光信号增强处理,利用深度学习算法进行弱光信号检测过程中的寻优控制,使得整个信号检测过程保证在恒虚警概率。仿真结果表明,该方法进行水下激光通信系统弱光信号检测对多径衰落的抑制能力较好,不同信噪比环境下误码率较低,当信噪比增大至90 dB时,其误码率仅为10-5,且其检测时间小于10 ms,优于对比方法,具有更大的应用价值。
Research on detection method of weak light signal in underwater laser communication system based on deep learning
The weak light signal of underwater laser communication system is interfered by spectrum and mul-tipath,and the detection accuracy is not high.In order to improve the stability of underwater laser communication,a detection method of weak light signal of underwater laser communication system based on deep learning is proposed.The spatial beam forming method is used to construct the underwater laser communication acquisition and multi-layer spectrum decomposition model,and the beam characteristics of the signal are enhanced.The radio frequency tag distri-bution information and multipath information of the weak optical signal are estimated by combining the time reversal technology,and the weak optical signal of the underwater laser communication system is enhanced by using multipath fading suppression.The optimization control is carried out in the detection process of the weak optical signal by combi-ning the deep learning algorithm,so that the whole signal detection process is guaranteed to have a constant false alarm probability.The simulation results show that this method has a good ability to suppress multipath fading when detecting weak light signals in underwater laser communication systems.The error rate is low in different signal-to-noise ratio environments.When the signal-to-noise ratio increases to 90 dB,the error rate is only 10-5,and the detection time is less than 10 ms,which is superior to the comparison method and has greater application value.

deep learningUnderwater laser communication systemWeak light signalDetectionSNR

杜玉红、侯守明

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河南理工大学鹤壁工程技术学院,河南鹤壁 458030

河南理工大学计算机科学与技术学院,河南焦作 454003

深度学习 水下激光通信系统 弱光信号 检测 信噪比

河南省自然科学基金

22300420482

2024

激光杂志
重庆市光学机械研究所

激光杂志

CSTPCD北大核心
影响因子:0.74
ISSN:0253-2743
年,卷(期):2024.45(6)
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