Robust Contingency Planning For Multiple ISR Sensors

An essential capability of an information gathering architecture is the ability to adapt to dynamic re-tasking requirements (i.e. the ability to manage real-time moding, pointing, and directing of ISR sensors) commensurate with the decision-making timescale of tactical, battlefield operations. Reliance on reactive dynamic re-tasking alone is likely to require excessive sensing resources to meet tactical time-lines. A more efficient and robust approach is to develop semi-automated planning tools thatare capable of generating collection plans that hedge against uncertainty to provide robustness to expected contingencies (e.g., pop-up tasks, degradation of sensor performance, loss of a sensor, etc.). ALPHATECH is uniquely qualified to address theproblem of robust collection planning for effective dynamic re-tasking of ISR assets. ALPHATECH has been at the forefront of developing new technology for ISR collection planning and management. A significant challenge confronting the generation of robustplans is properly balancing between the accommodation of contingencies, performing wide area surveillance, and collecting against known targets. This proposal addresses the challenge of robust collection planning using an innovative optimization approachbalancing the potentially conflicting objectives of an ISR collection manager to establish a value function enabling automatic generation of robust collection plans. The development of robust plan generation techniques has wide applicability tointelligent traffic control, automated manufacturing, airline scheduling, package delivery scheduling and a large variety of remote sensing applications including military intelligence, surveillance, reconnaissance, and strike missions. ISR planning, inparticular, is rapidly becoming an important capability requirement across all DoD services, especially as UAVs become more prevalent. DoD planning is faced with a high degree of uncertainty, complicated by an intelligent adversary with unknown intent,making robust plan generation a critical extension to the state-of-the-art.