Resource Allocation using Market-Based Optimization (RAMBO)

Missile defense takes place in a dynamic, uncertain environment. Multi-raid attacks threaten to overload sensor capabilities, leaving fewer sensor resources free to observe each target complex. Moreover, the shift in emphasis toward early intercept, coupled with the growing importance of short- and medium-range threats, reduces the time available for detection, tracking, and discrimination, because engagement decisions must be made more quickly. These factors increase the need for effective sensor resource management to squeeze the maximum amount of useful information out of an overloaded sensor network. What is needed is a solution that can optimally assign a large number of heterogeneous sensors to a large number of incoming threats fast enough for early intercept, while meeting dynamic constraints on sensor availability and physical constraints. Here, we propose Resource Allocation using Market-Based Optimization (RAMBO), to provide dynamic planning and scheduling of sensors to maximize sensor quality of service while minimizing sensor uncertainty. This approach applies market-based optimization, using solutions from economic theory and game theory, to sensor resource management by creating an artificial market for sensor capabilities and information. With a properly designed market, we can engineer a system that ensures an optimal allocation of sensors and resources.