Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer

We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistatic radars. We use GPR to calculate the posterior predictive distribution on the labels (i.e., duct height) from both noise‐free and noise‐contaminated array of propagation factors. For duct height inference from noise‐contaminated propagation factors, we compare a naïve approach, utilizing one random sample from the input distribution (i.e., disregarding the input noise), with an inverse‐variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a “ground truth” distribution, which is approximated using a large number of Monte‐Carlo samples. The ability of GPR to yield accurate and fast duct height predictions using a few training examples indicates the suitability of the proposed method for real‐time applications. Key Points We propose a machine learning approach for real‐time characterization of EM propagation within the marine atmospheric boundary layer Gaussian process regression can be effective in predicting EM duct height from propagation factor data and quantifying its uncertainty Few samples using inverse‐variance mixing proportions can roughly approximate the predictive distribution of noisy propagation factor data