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

## TL;DR

This paper demonstrates that Gaussian process regression can accurately and efficiently estimate electromagnetic duct heights in the marine atmospheric boundary layer from sparse and noisy data, suitable for real-time radar applications.

## Contribution

The paper introduces a GPR-based method for inferring EM duct heights from sparse, noisy measurements, including a comparison of naive and inverse-variance weighted approaches.

## Key findings

- GPR provides accurate duct height estimates from limited data.
- Inverse-variance weighting improves predictions with noisy inputs.
- Method is suitable for real-time EM ducting inference.

## Abstract

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.