Incorporating prior knowledge of urban scene spatial structure in aperture code designs for surveillance systems

Two major missions of Surveillance systems are imaging and ground moving target indication (GMTI). Recent advances in coded aperture electro optical systems have enabled persistent surveillance systems with extremely large fields of regard. The areas of interest for these surveillance systems are typically urban, with spatial topologies having a very definite structure. We incorporate aspects of a priori information on this structure in our aperture code designs to enable optimized dealiasing operations for undersampled focal plane arrays. Our framework enables us to design aperture codes to minimize mean square error for image reconstruction or to maximize signal to clutter ratio for GMTI detection. In this paper we present a technical overview of our code design methodology and show the results of our designed codes on simulated DIRSIG mega-scene data.