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.
We present a technique for interferometric processing of extreme high-squint synthetic aperture radar (SAR) target images. The technique is an improvement over conventional SAR interferometry, as it takes into account the change in relative phase between channels that occur as the radar moves through the synthetic aperture, which can be significant for forward-looking data acquisition geometry. We apply the technique to simulate data from a variety of imaging cases and show that, unlike the conventional interferometric technique, the proposed technique is robust to synthetic aperture length, scatterer elevation, and scatterer dimension in forward-looking scenarios. The technique may be used for various applications related to situational awareness, such as remote sensing for collision avoidance or forward-looking reconnaissance.