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).

Free space optical communications (FSO), which make use of the visible and infrared spectrum for data transmission, offer significant advantages such as a very high data rate, security and immunity, low cost of installation and ease of use without any license restrictions. However, a significant challenge for FSO systems is their inherent constraints due to environmental conditions and especially atmospheric turbulence. This paper focuses on the experimental performance analysis of a real FSO system in a maritime environment. We propose a new model which allows an FSO link performance estimation over sea and depends upon point measurements of environmental parameters. The Received Signal Strength Indicator (RSSI) has been measured and a second-order polynomial has been constructed using regression modeling to quantify its relation with macroscopic environmental parameters collected by a weather station. This model has then been validated against real meteorological data over different period of times and exhibited a reasonably strong correlation. Atmospheric turbulence has been determined using bulk estimates of the structure index parameter extracted from the same meteorological data, and thus allowed for a statistical correlation between turbulence and RSSI. In the second part of the paper, the probability distribution of the RSSI data has been investigated and the Kullback-Leibler (KL) divergence has been used to investigate the difference between probability distributions over the same variable. As an illustrative example of the process, the Weibull, Lognormal and Gamma distributions have been evaluated against the RSSI data probability distribution and the latter has proved to exhibit the best fit.

A laser beam propagating in the free space suffers numerous degradation effects. In the context of free space optical communications (FSOCs), this results in reduced availability of the link. This study provides a comprehensive comparison between six machine learning (ML) regression algorithms for modeling the refractive index structure parameter ($C_n^2)$. A single neural network (ANN), a random forest (RF), a decision tree (DT), a gradient boosting regressor (GBR), a k-nearest neighbors (KNN) and a deep neural network (DNN) model are applied to estimate $C_n^2$ from experimentally measured macroscopic meteorological parameters obtained from several devices installed at the Naval Postgraduate School (NPS) campus over a period of 11 months. The data set was divided into four quarters and the performance of each algorithm in every quarter was determined based on the R² and the RMSE metric. The corresponding RMSE were 0.091 for ANN, 0.064 for RF, 0.075 for GBR, 0.073 for KNN, 0.083 for DT and 0.085 for DNN. The second part of the study investigated the influence of atmospheric turbulence in the availability of a notional FSOC link, by calculating the outage probability (P_out) assuming a gamma gamma (GG) modeled turbulent channel. A threshold value of 99% availability was assumed for the link to be functional. A DNN classification algorithm was then developed to model the link status (On-Off) based on the previously mentioned meteorological parameters.

In this work, a 4D hyperchaotic hyperjerk system, with better results for its Lyapunov exponents and Kaplan–Yorke dimension regarding other systems of this family, as well as its circuit implementation, is presented. Hyperchaotic hyperjerk systems depict complex dynamical behavior in a high-dimensional phase space with n ≥ 4, offering robustness against many types of attacks in private communications. For this reason, an adaptive controller in order to achieve global chaos synchronization of coupled 4D hyperchaotic hyperjerk systems with unknown parameters is designed. The adaptive results in this work are proved using Lyapunov stability theory and the effectiveness of the proposed synchronization scheme is confirmed through the simulation results.

Efforts to develop high-energy laser (HEL) weapons that are capable of being integrated and operated aboard naval platforms have gained an increased interest, partially due to the proliferation of various kinds of unmanned systems that pose a critical asymmetric threat to them, both operationally and financially. HEL weapons allow for an unconstrained depth of magazine and cost exchange ratio, both of which are essential characteristics to effectively oppose small unmanned systems, compared to their kinetic weapons counterparts. However, HEL performance is heavily affected by atmospheric conditions between the weapon and the target; therefore, the more precise and accurate the atmospheric characterization, the more accurate the performance estimation of the HEL weapon. To that end, the Directed Energy Group of the Naval Postgraduate School (NPS) is conducting experimental, theoretical and computational research on the effects of atmospheric conditions on HEL weapon efficacy. This paper proposes a new approach to the NPS laser performance code scheme, which leverages artificial neural networks (ANNs) for the prediction of optical turbulence strength. This improvement could allow for near real-time and location-independent HEL weapon performance estimation. Two experimental datasets, which were obtained from the NPS facilities, were utilized to perform regression modeling using an ANN, which achieved a decent fit (R² = 0.75 for the first dataset and R² = 0.78 for the second dataset).

The performance of a free-space optical (FSO) communications link suffers from the deleterious effects of weather conditions and atmospheric turbulence. In order to better estimate the reliability and availability of an FSO link, a suitable distribution needs to be employed. The accuracy of this model depends strongly on the atmospheric turbulence strength which causes the scintillation effect. To this end, a variety of probability density functions were utilized to model the optical channel according to the strength of the refractive index structure parameter. Although many theoretical models have shown satisfactory performance, in reality they can significantly differ. This work employs an information theoretic method, namely the so-called Jensen–Shannon divergence, a symmetrization of the Kullback–Leibler divergence, to measure the similarity between different probability distributions. In doing so, a large experimental dataset of received signal strength measurements from a real FSO link is utilized. Additionally, the Pearson family of continuous probability distributions is also employed to determine the best fit according to the mean, standard deviation, skewness and kurtosis of the modeled data.