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Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary Layer
Radio Science ( IF 1.6 ) Pub Date : 2020-06-01 , DOI: 10.1029/2019rs006890
Hilarie Sit 1 , Christopher J. Earls 1, 2
Affiliation  

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.

中文翻译:

用于估计海洋大气边界层内电磁管道的高斯过程回归

我们表明,高斯过程回归 (GPR) 可用于从双基地雷达环境中的稀疏采样传播因子推断海洋大气边界层 (MABL) 内的电磁 (EM) 管道高度。我们使用 GPR 从无噪声和受噪声污染的传播因子阵列计算标签上的后验预测分布(即管道高度)。对于来自噪声污染传播因素的管道高度推断,我们比较了一种朴素的方法,利用来自输入分布的一个随机样本(即忽略输入噪声),与逆方差加权方法,利用几个随机样本来估计真实预测分布。将这两种方法得到的后验预测分布与“基本事实”分布进行比较,这是使用大量蒙特卡洛样本来近似的。GPR 使用一些训练示例产生准确和快速的管道高度预测的能力表明所提出的方法适用于实时应用。
更新日期:2020-06-01
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