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适应性曝光优化用于通过多模式特征学习和实向模拟通道仿真进行水下光学摄像头通信

Jiongnan Lou¹, Xun Zhang², Haifei Shen¹

¹School of Information Engineering, Huzhou University, Huzhou 313000, China.

Sensors (Basel, Switzerland)

|October 29, 2025

概括

本研究引入了用于水下光学摄像机通信 (UOCC) 的自适应系统,该系统在变化条件下提高了可靠性. 这种新方法改善了信号噪声比 (SNR) 以实现更清晰的水下数据传输.

科学领域:

在水下进行光通信.
自动水下车辆 (AUV) 是一种自动驾驶的水下车辆.
机器学习用于环境传感.

背景情况:

水下光学摄像头通信 (UOCC) 对AUV至关重要,但与环境变化 (度,散射,照明) 斗争.
固定摄像头设置 (曝光时间,ISO灵敏度) 限制了UOCC的可靠性,因为水中条件不断变化.
以前用于参数预测的深度学习方法缺乏环境意识和适应能力.

研究的目的:

为UOCC开发一种适应性系统,克服固定设置的局限性,提高通信可靠性.
引入一个实时到模拟到部署的框架,结合混合CNN-MLP模型 (HCMM) 来进行动态参数预测.
通过基于环境和摄像头状态实现实时适应性重新配置来提高UOCC的性能.

主要方法:

开发了一个物理校准的模拟平台,用于可重复的光通道模拟.
介绍了一个混合CNN-MLP模型 (HCMM),它融合了光学图像,环境状态和摄像头配置.
通过模拟和部署嵌入式硬件来验证HCMM的性能,以便实时适应.

主要成果:

该HCMM实现了大大提高参数预测准确度,将RMSE降低到0.23-0.33.
嵌入式硬件上的实时自适应性重新配置导致高达8.5dB的SNR增益.
拟议的系统在动态场景中表现优于静态参数系统和先前的深度学习基线.

结论:

环境意识的多模式学习,加上光通道仿真,为实际的UOCC提供了可扩展和强大的解决方案.
适应性系统提高了UOCC可靠性,用于AUV定位,检查和基于激光的通信等应用.
这一框架为在多样化和具有挑战性的水生环境中更可靠的水下数据交换铺平了道路.

关键词:

在CMOS成像中使用CMOS成像.适应性暴露适应性暴露多式模式特征学习学习光学通道模拟的光学通道模拟水下光学摄像头通信

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