Michalopoulou Zoi-Heleni, Gerstoft Peter, Caviedes-Nozal Diego
Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey 07102, USA.
University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093-0238, USA.
JASA Express Lett. 2021 Jun;1(6):064801. doi: 10.1121/10.0005069.
For a sparsely observed acoustic field, Gaussian processes can predict a densely sampled field on the array. The prediction quality depends on the choice of a kernel and a set of hyperparameters. Gaussian processes are applied to source localization in the ocean in combination with matched-field processing. Compared to conventional processing, the denser sampling of the predicted field across the array reduces the ambiguity function sidelobes. As the noise level increases, the Gaussian process-based processor has a distinctly higher probability of correct localization than conventional processing, due to both denoising and denser field prediction.
对于稀疏观测的声场，高斯过程可以预测阵列上密集采样的场。预测质量取决于核函数和一组超参数的选择。高斯过程与匹配场处理相结合应用于海洋中的源定位。与传统处理相比，预测场在阵列上的更密集采样降低了模糊函数旁瓣。随着噪声水平的增加，基于高斯过程的处理器由于去噪和更密集的场预测，具有比传统处理明显更高的正确定位概率。
