Deep Learning for Range Localization via Over-Water Electromagnetic Signals

Abstract

Neural networks are widely applied in domains such as image processing, natural language processing, and time series forecasting. However, neural networks have seen less use in problems arising in the physical sciences. This is unfortunate, since the physical domain has a wealth of problems that can benefit from application of neural networks. These problems hold substantial significance to many areas such as manufacturing, material science, and many others. In the current text we demonstrate that knowledge of the physical systems of interest can be combined with effective data preprocessing and neural network training to achieve prediction effectiveness which is greater than the sum of its parts. In particular, we study the challenging problem of range estimation from the measurement of electromagnetic scattering of radio waves reflected off the surface of the ocean and the atmosphere. Our key finding is a that good performance can only be achieved by combining physical principles with careful data preprocessing and network training.