Single and Multi-Sensor MUSIC Algorithms for CSO Super-Resolution

PRA proposes to develop a super-resolution fusion algorithm for midcourse detection and tracking of closely spaced objects as observed by single and multiple IR sensor systems in the SBIRS Low system. This algorithm will be based on application of the MUSIC algorithm to optical sensors, and combined with novel filtering algorithms. It will provide super-resolution, while operating under minimal hypotheses about the IR signatures of the objects in the CSO cloud, a key requirement. The super-resolution gained for single sensor platforms can be further enhanced using multi-sensor platforms. The multi-sensor algorithm will be designed to use the distributed IR sensor system as a distributed computing platform, minimizing intra-sensor communication requirements. The proposed algorithms address a critically important detection and tracking problem in missile defense as the scenario's under investigation involve rapidly increasing numbers of objects with wider signature ranges. This requires signal processing methods which are as much as possible prior-free. Similar requirements also abound in multi-sensor surveillance platforms and in multi-source medical imaging, but with different geometric constraints and noise sources. The algorithm developed here for single sensor and its distributed extension to multi-sensor platforms can be migrated to other such arenas.