Multistatic radar change detection using sparse imaging methods

This paper describes a sparse imaging approach for estimating change images from a multistatic radar data. In our application, antennas are arranged around the perimeter of a surveillance region. This provides large angular diversity but a very small angular sampling over the large aperture. Furthermore, due to application constraints, the scene is interrogated with limited frequency diversity. We address the change image estimation problem using a compressed sensing reconstruction method to estimate the high-dimensional signal from the much lower dimensional measurement. We show with real collected data the sparseness model enables excellent imaging with very limited spatial and frequency sampling.