Scalable Data and Sensor Fusion Through Optimal Solutions of Multiple-Agent Systems.

We address the problem of finding an unbiased estimate of the battlefield state given that the data available is dynamic, noisy, and given in a multiplicity of representations. The approach proposed in the study is unique because it does not attempt to transform the data to a common representation. Rather we establish a framework which we call the Multiple Agent Hybrid Estimation Architecture MAHEA in which we allow heterogeneous data to flow between individual agents in the network to improve their individual estimates of the current plant state. For each MAHEA agent, we can construct a special optimization criterion for the battlefield estimation optimization problem which we call the Estimation Lagrangian. The type of Lagrangian that we need to construct is special for the plant estimation problem in that we want the Lagrangian to be 0 at each point where the agent has reached the desired estimate of the plant state. Such lagrangians are not suitable for most control problems. We also develop mechanisms to solve the problems of agent synchronization and how agents with different models can produce coherent synchronous and compatible common view estimates of the battlefield. Our solution to these problems uses the Noether invariance conditions in a novel way.