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用于水下光无线链路的抗失准传播模型。

Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links.

机构信息

INESC TEC-Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal.

Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.

出版信息

Sensors (Basel). 2022 Dec 29;23(1):359. doi: 10.3390/s23010359.

Abstract

This paper proposes a multiple-lens receiver scheme to increase the misalignment tolerance of an underwater optical wireless communications link between an autonomous underwater vehicle (AUV) and a sensor plane. An accurate model of photon propagation based on the Monte Carlo simulation is presented which accounts for the lens(es) photon refraction at the sensor interface and angular misalignment between the emitter and receiver. The results show that the ideal divergence of the beam of the emitter is around 15° for a 1 m transmission length, increasing to 22° for a shorter distance of 0.5 m but being independent of the water turbidity. In addition, it is concluded that a seven-lense scheme is approximately three times more tolerant to offset than a single lens. A random forest machine learning algorithm is also assessed for its suitability to estimate the offset and angle of the AUV in relation to the fixed sensor, based on the power distribution of each lens, in real time. The algorithm is able to estimate the offset and angular misalignment with a mean square error of 5 mm (6 mm) and 0.157 rad (0.174 rad) for a distance between the transmitter and receiver of 1 m and 0.5 m, respectively.

摘要

本文提出了一种多镜头接收器方案,以提高自主水下航行器(AUV)和传感器平面之间水下光无线通信链路的失准容限。提出了一种基于蒙特卡罗模拟的精确光子传播模型,该模型考虑了传感器界面处的透镜(s)光子折射以及发射器和接收器之间的角度失准。结果表明,对于 1m 的传输长度,发射器光束的理想发散角约为 15°,对于较短的 0.5m 距离,发散角增加到 22°,但与水浊度无关。此外,研究还得出结论,与单个镜头相比,七个镜头方案对偏移的容忍度大约提高了三倍。还评估了随机森林机器学习算法,以根据每个镜头的功率分布,实时评估基于功率分布的算法是否适合估计 AUV 相对于固定传感器的偏移量和角度。对于发射器和接收器之间的距离为 1m 和 0.5m,该算法分别能够以 5mm(6mm)和 0.157rad(0.174rad)的均方误差估计偏移量和角向失准。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4a3/9824780/f2b4b8caf769/sensors-23-00359-g001.jpg

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