采样(信号处理)
高斯分布
海床
计算机科学
航程(航空)
高斯过程
降噪
光圈(计算机存储器)
模式识别(心理学)
声学
人工智能
地质学
计算机视觉
物理
材料科学
海洋学
滤波器(信号处理)
量子力学
复合材料
作者
Zoi-Heleni Michalopoulou,Christina Frederick
出处
期刊:Journal of the Acoustical Society of America
[Acoustical Society of America]
日期:2023-10-01
卷期号:154 (4_supplement): A340-A340
被引量:1
摘要
Workshop ’97 data are employed for seabed classification and source range estimation. The data are acoustic fields computed at vertically separated receivers for various ranges and three different environments. Gaussian Processes are applied for denoising the data and predicting the field at virtual receivers, sampling the water column densely within the array aperture. The enhanced fields are then used in combination with machine learning in order to map the signals to one of 15 sediment-range classes (corresponding to three environments and five ranges). In prior work, the classification results after using Gaussian Processes for denoising were demonstrated to be superior to those when noisy workshop data are employed. Here, we explore optimal sampling strategies (e.g., nonuniform sampling, subsampling) for inducing sparsity in the correlation matrices that are based on hydrophone locations, and compare these with uniform sampling that was used in our prior work.
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