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A surveillance system needs to accurately locate and identifjt not only single targets, but also groups of targets engaged in a common activity. Most existing tracking systems are capable of tracking individual targets quite accurately; however, they fail to use information related to group behavior in order to improve these estimates. Additionally, in wide area surveillance situations a military operator is required to sort through hundreds to thousands of individual targets in order to develop an understanding of the situation. Having the ability to collapse the behavior of individual targets into a common, coordinated motion can greatly enhance the productively and situational awareness of the operator. Our long-term approach to solving this problem is to develop an understanding of how to dejine a group and then to understand the inter-relationships between the various characteristics that describe a group. Then using this information, we will be able to partition the set of target into groups that can be aggregated over the entire military force hierarchy. This goal of this paper is to describe an approach that is based upon genetic algorithms for solving the military force hierarchy problem. This paper will describe the underlying genetic algorithm, scoring firnction, and some initial results.

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This paper introduces an active-passive networked multiagent system framework, which consists of agents subject to exogenous inputs (active agents) and agents without any inputs (passive agents), and analyze its convergence using Lyapunov stability. Apart from the existing relevant literature, where either none of the agents are subject to exogenous inputs (i.e., average consensus problem) or all agents are subject to these inputs (i.e., dynamic average consensus problem), the key feature of our approach is that the states of all agents converge to the average of the exogenous inputs applied only to the active agents, where these inputs may or may not overlap within the active agents.