The Linear Sampling Method is an imaging technique that reconstructs target shape via a series of beamforming operations without using linear scattering assumptions. The norm of the solution is typically used to determine which pixels are inside the target support. There has not been much study of how to use the phase of the solution to aid in target identification. In this study, we explore using the solution phase to classify targets according to their electrical properties via a machine learning approach. We implement a support vector machine, apply it to imagery from simulated target data, and quantify classification accuracy.
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