Feature-Enhanced, Model-Based Sparse Aperture Imaging

This report summarizes the research and activities under the Revolutionary Automatic Target Recognition and Sensor Research (RASER) Grant FA8650-04-1-1719 on the topic of Feature-Enhanced, Model-Based Sparse Aperture Imaging. This project has been motivated by a number of emerging military applications where we are faced with sparse apertures. Examples include wide-angle imaging, foliage penetration radar, bistatic imaging, and passive radar imaging. While the possibility of exploiting such rich sensor data presents remarkable opportunities for surveillance, image formation and visualization from sparse aperture data poses significant challenges. The focus of our research effort has been to meet these challenges and develop principled and practical sparse aperture imaging techniques which generate enhanced imagery facilitating visual or automatic interpretation of the underlying scenes. In this report, we provide a picture of the activities and progress that have occurred in this project. In particular, we include both basic factual information on personnel, publications, and interactions, as well as a brief description of our research activities and how they relate to the statement of work included in our proposal. This report summarizes the research and activities under the Revolutionary Automatic Target Recognition and Sensor Research (RASER) Grant FA8650-04-1-1719 on the topic of Feature-Enhanced, Model-Based Sparse Aperture Imaging. This research has been performed at the Massachusetts Institute of Technology. The primary researchers who have led this research program are Dr. Mujdat Cetin, Prof. Alan Willsky, and Dr. John Fisher. This project has been motivated by a number of emerging military applications where we are faced with sparse apertures. Examples include wide-angle imaging, foliage penetration radar, bistatic imaging, and passive radar imaging.
The original document contains color images. All DTIC reproductions will be in black and white.