Institute of Physics, Eötvös Loránd University, Pázmány P. stny. 1A, 1117 Budapest, Hungary.
Department of Applied Analysis and Computational Mathematics & NumNet MTA-ELTE Research Group, Eötvös Loránd University, Pázmány P. stny. 1C, 1117 Budapest, Hungary.
Sensors (Basel). 2020 Dec 22;21(1):11. doi: 10.3390/s21010011.
The localization of multiple scattering objects is performed while using scattered waves. An up-to-date approach: neural networks are used to estimate the corresponding locations. In the scattering phenomenon under investigation, we assume known incident plane waves, fully reflecting balls with known diameters and measurement data of the scattered wave on one fixed segment. The training data are constructed while using the simulation package μ-diff in Matlab. The structure of the neural networks, which are widely used for similar purposes, is further developed. A complex locally connected layer is the main compound of the proposed setup. With this and an appropriate preprocessing of the training data set, the number of parameters can be kept at a relatively low level. As a result, using a relatively large training data set, the unknown locations of the objects can be estimated effectively.
利用散射波实现多散射物体的定位。一种最新的方法：使用神经网络来估计相应的位置。在所研究的散射现象中，我们假设已知的入射平面波、具有已知直径的全反射球和在一个固定段上的散射波的测量数据。训练数据是使用 Matlab 中的 μ-diff 模拟包构建的。进一步开发了广泛用于类似目的的神经网络的结构。复杂的局部连接层是所提出的设置的主要组成部分。通过这种方法和对训练数据集的适当预处理，可以将参数数量保持在相对较低的水平。结果，使用相对较大的训练数据集，可以有效地估计物体的未知位置。
