Solution of Inverse Problems in Electromagnetic and Optical Propagation Using Artificial Neural Networks

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In electromagnetic and optical propagation analysis, the forward problem denotes the calculation of fields and waves given the parameters of the media. The inverse problem is to calculate the target or media parameters from given or measured fields and waves. In the past, study of the forward problem has been done extensively. However there is a relative lack of systematic investigation of the electromagnetic and optical inverse problems because of the highly nonlinear relations between fields and the media parameters. With the use of recent Artificial Neural Networks (ANN), we have been successful in developing efficient and speedy algorithms to solve nonlinear inverse problems in fields and waves. Specifically we propose to apply and develop ANN algorithms and query learning techniques for the study of the following: 1. Inverse problems in microwave and optical scattering in remote sensing of agriculture and ice and snow will be investigated. Multiple scattering effects of discrete random media and rough surfaces will be included. The ANN algorithms and query learning techniques will be applied to both synthetic data as well as satellite and aircraft remote sensing measurements. 2. Fast acquisition of design parameters in the parametric frequency selective surface designs based on neural network inversion will be investigated. Constrained inversion for iterative acquisition of 2D/3D surfaces using bitmap representations will be included. These techniques will also be applied to identify new surface elements that will provide unconventional frequency responses.