We explore the impacts of adverse weather on the propagation of a pulsed 1.5um laser source over a 1km maritime channel. The propagation path along this channel is well-instrumented with sensors to measure standard weather conditions (wind, temperature, humidity and rainfall), visibility and atmospheric turbulence. Data collected to characterize the propagation path are used to initialize channel modeling and predict performance of 1.5um propagation. A high-speed detector and a camera located at the target board recorded temporal and spatial effects of rain on the propagated laser beam. The data are analyzed for pulse width, beam profile, beam wander as a function of rainfall and compared to the channel model.
An accurate forecast of atmospheric turbulence is needed to characterize the performance of free-space electro-optical systems. Atmospheric turbulence physics is intricate and stochastic and relies on an assortment of assumptions, making modeling difficult to formulate in a rigorous first principles manner. Machine learning (ML) techniques have been shown to parameterize complex relationships in large datasets and are able to more accurately predict response variables than standard regression methods. This study applies machine learning techniques to develop a model that forecasts the refractive index structure parameter, 𝐶𝑛 2. Measurements of 𝐶𝑛 2 were obtained from a field experiment along with meteorological observations. Several machine learning models were created and compared to optical scintillometer output and current atmospheric turbulence models. The ML based models are shown to generate predictions of 𝐶𝑛 2 that are more highly correlated to observed 𝐶𝑛 2 than physics-based formulations.
An accurate model and parameterization of aerosol concentration is needed to predict the performance of electro-optical
imaging systems. Current models have been shown to vary widely in their ability to accurately predict aerosol size
distributions and subsequent scattering properties of the atmosphere. One of the more prevalent methods for modeling
particle size spectra consists of fitting a modified gamma function to measurement data, however this limits the
distribution to a single mode. Machine learning models have been shown to predict complex multimodal aerosol particle
size spectra. Here we establish an empirical model for predicting aerosol size spectra using machine learning techniques.
This is accomplished through measurements of aerosols size distributions over the course of eight months. The machine
learning models are shown to extend the functionality of Advanced Navy Aerosol Model (ANAM), developed to model
the size distribution of aerosols in the maritime environment.
An accurate model and parameterization of fog is needed to increase the reliability and usefulness of electro-optical systems in all relevant environments. Current models vary widely in their ability to accurately predict the size distribution and subsequent optical properties of fog. The Advanced Navy Aerosol Model (ANAM), developed to model the distribution of aerosols in the maritime environment, does not currently include a model for fog. One of the more prevalent methods for modeling particle size spectra consists of fitting a modified gamma function to fog measurement data. This limits the fog distribution to a single mode. Here we establish an empirical model for predicting complicated multimodal fog droplet size spectra using machine learning techniques. This is accomplished through careful measurements of fog in a controlled laboratory environment and measuring fog particle size distributions during outdoor fog events.
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