There has been increased impetus to leverage Small Unmanned Aerial Systems (SUAS) for collaborative sensing applications in which many platforms work together to provide critical situation awareness in dynamic environments. Such applications require critical sensor observations to be made at the right place and time to facilitate the detection, tracking, and classification of ground-based objects. This further requires rapid response to real-world events and the balancing of multiple, competing mission objectives. In this context, human operators become overwhelmed with management of many platforms. Further, current automated planning paradigms tend to be centralized and don’t scale up well to many collaborating platforms. We introduce a decentralized approach based upon information-theory and distributed fusion which enable us to scale up to large numbers of collaborating Small Unmanned Aerial Systems (SUAS) platforms. This is exercised against a military application involving the autonomous detection, tracking, and classification of critical mobile targets. We further show that, based upon monte-carlo simulation results, our decentralized approach out-performs more static management strategies employed by human operators and achieves similar results to a centralized approach while being scalable and robust to degradation of communication. Finally, we describe the limitations of our approach and future directions for our research.