733 research outputs found
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.Comment: 13 pages, 7 figure
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 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. 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.Comment: 15 pages, 6 figure
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