Code and data from: Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer
Sit, Hilarie; Earls, Christopher J
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 bistaticradars. These propagation factors are simulated using PETOOL, developed by Ozgun et al. 2011, and the datasets for the three cases that correspond to the different sparse sampling techniques can be found in the data folder. 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 naive 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. We use Python 3.6.4 and scikit-learn 0.20.2. The ability of GPR to yield accurate duct height predictions using few training examples, along with its inference speed, indicates the suitability of the proposed method for real-time applications. This is the dataset and code that supports this work.
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The authors gratefully acknowledge ONR Division 331 and Dr. Steve Russell for the financial support of this work through grant N00014-19-1-2095.
gaussian process regression; marine atmospheric boundary layer; evaporation ducts; inverse problem; machine learning
Sit, H., Earls, C.J. (2019) “Gaussian process regression for estimating EM ducting within the marine atmospheric boundary layer,” Radio Science, American Geophysical Union, IN REVIEW.