Market-Based Optimization for Detection and Tracking of Resident Space Objects

Future space-based sensors have more complex data processing requirements and thus need more efficient sensor management tools to ensure their effective operation. More-over, the dynamic, real-time nature of the domain requires a solution capable of adaptive scheduling. Here, we propose a market based approach to optimize the detection and track-ing of resident space objects that encompasses the sensor system as a whole, including the network resources and computational power that support sensor tasks. Our market-based optimization approach applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guaran-tees an optimal allocation of resources throughout the sensor network. This approach was chosen in large part for its superior computational efficiency, based on the mathematical foundations of economic theory. I