Progress in laser propagation in a maritime environment at the Naval Research Laboratory

Gilbreath, G. C.; Rabinovich, William S.; Moore, Christopher I.; Burris, H. R.; Mahon, Rita; Grant, Kevin; Goetz, Peter G.; Murphy, James L.; Suite, Michele R.; Stell, Mena F.; Swingen, M. L.; Wasiczko, Linda M.; Restaino, Sergio R.; Wilcox, Christopher C.; Andrews, Jonathan R.; Scharpf, William J.

doi:10.1117/12.633390

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…These studies included both passive (spotlight) and active (laser beam) intensity and angle-of-arrival-based turbulence monitoring. To facilitate their asymmetric studies, the NRL developed a multiple quantum well (MQW) modulating retroreflector, which supports high-rate data transfer with very low power requirements [16]. A second approach is the cat's eye (CEMRR), which consists of a series of modulators.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning for Refractive Index Structure Parameter Modeling

Lionis

Peppas

Nistazakis

et al. 2023

QuBS

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The Hellenic Naval Academy (HNA) reports the latest results from a medium-range, near-maritime, free-space laser-communications-testing facility, between the lighthouse of Psitalia Island and the academy’s laboratory building. The FSO link is established within the premises of Piraeus port, with a path length of 2958 m and an average altitude of 35 m, mainly above water. Recently, the facility was upgraded through the addition of a BLS450 scintillometer, which is co-located with the MRV TS5000/155 FSO system and a WS-2000 weather station. This paper presents the preliminary optical turbulence measurements, collected from 24 to 31 of May 2022, alongside the macroscopic meteorological parameters. Four machine-learning algorithms (random forest (RF), gradient boosting regressor (GBR), single layer (ANN), and deep neural network (DNN)) were utilized for refractive-index-structural-parameter regression modeling. Additionally, another DNN was used to classify the strength level of the optical turbulence, as either strong or weak. The results showed very good prediction accuracy for all the models. Specifically, the ANN algorithm resulted in an R-squared of 0.896 and a mean square error (MSE) of 0.0834; the RF algorithm also gave a highly acceptable R-squared of 0.865 and a root mean square error (RMSE) of 0.241. The Gradient Boosting Regressor (GBR) resulted in an R-squared of 0.851 and a RMSE of 0.252 and, finally, the DNN algorithm resulted in an R-squared of 0.79 and a RMSE of 0.088. The DNN-turbulence-strength-classification model exhibited a very acceptable classification performance, given the highly variability of our target value (Cn2), since we observed a predictive accuracy of 87% with the model.

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Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning for Refractive Index Structure Parameter Modeling

Lionis

Peppas

Nistazakis

et al. 2023

QuBS

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“…Their main goal has been atmospheric propagation research and photonic components development in order to characterize and overcome the limitations due to turbulence effects. In doing so, NRL has developed a maritime lasercomm test facility (LCTF), consisting of facilities 16-km apart on both sides of Chesapeake Bay, equipped with instruments to measure transmission, the scintillation index and angle of arrival of the beam [7][8][9][10][11][12][13]. During another trial (FATMOSE), high resolution images were collected by point sources at a range of 15.7 km, along with turbulence related atmospheric parameters that provided statistical information on the mean and variance of the atmospheric point spread function and the associated modulation transfer function during a series of consecutive frames [14].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Accurate Received Optical Power Prediction of an FSO Link over a Maritime Environment

et al. 2021

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The performance prediction of an optical communications link over maritime environments has been extensively researched over the last two decades. The various atmospheric phenomena and turbulence effects have been thoroughly explored, and long-term measurements have allowed for the construction of simple empirical models. The aim of this work is to demonstrate the prediction accuracy of various machine learning (ML) algorithms for a free-space optical communication (FSO) link performance, with respect to real time, non-linear atmospheric conditions. A large data set of received signal strength indicators (RSSI) for a laser communications link has been collected and analyzed against seven local atmospheric parameters (i.e., wind speed, pressure, temperature, humidity, dew point, solar flux and air-sea temperature difference). The k-nearest-neighbors (KNN), tree-based methods-decision trees, random forest and gradient boosting- and artificial neural networks (ANN) have been employed and compared among each other using the root mean square error (RMSE) and the coefficient of determination (R2) of each model as the primary performance indices. The regression analysis revealed an excellent fit for all ML models, indicative of their ability to offer a significant improvement in FSO performance modeling as compared to traditional regression models. The best-performing R2 model found to be the ANN approach (0.94867), while random forests achieved the most optimal RMSE result (7.37).

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