Imaging in high clutter environments

Four microwave imaging methods are presented with the goal of suppressing natural clutter from such images. Unlike traditional clutter filtering approaches, based on a known clutter distribution, imaging algorithms aim to suppress any scattering mechanism not “stable” across all sensor locations. The four methods considered are (a) Coherence factor correction, (b) Model-based correction, (c) Adaptive sidelobe reduction (apodization), and (d) Image sparsity optimization (compressive sensing). In all cases, a clearer image is attained. However, image sparsity optimization leads to significantly sharper images. The images are actually super-resolved and are improved subject to available CPU time and/or data additions. Simulated and measured imaging examples are presented to demonstrate the stated conclusions.