U nmanned aerial vehicles (UAVs) are acquiring an increased level of autonomy as more complex mission scenarios are envisioned [1]. For example, UAVs are being used for intelligence, surveillance, and reconnaissance missions as well as to assist humans in the detection and localization of wildfires [2], tracking of moving vehicles along roads [3], [4], and performing border patrol missions [5]. A critical component for networks of autonomous vehicles is the ability to detect and localize targets of interest in a dynamic and unknown environment. The success of these missions hinges on the ability of the algorithms to appropriately handle the uncertainty in the information of the dynamic environment and the ability to cope with the potentially large amounts of communicated data that will need to be broadcast to synchronize information across networks of vehicles. Because of their relative simplicity, centralized mission management algorithms have previously been developed to create a conflict-free task assignment (TA) across all vehicles. However, these algorithms are often slow to react to changes in the fleet and environment and require high bandwidth communication to ensure a consistent situational awareness (SA) from distributed sensors and also to transmit detailed plans back to those sensors. More recently, decentralized decision-making algorithms have been proposed [6]-[8] that reduce the amount of communication required between agents and improve the robustness and reactive ability of the overall system to bandwidth limitations and fleet, mission, and environmental variations. These methods focus on individual agents generating and maintaining their own SA and TA, relying on periodic intervehicle

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Information consensus in sensor networks has received much attention due to its numerous applications in distributed decision making. This paper discusses the problem of a distributed group of agents coming to agreement on a probability vector over a network, such as would be required in a decentralized estimation of state transition probabilities or agreement on a probabilistic search map. Unique from other recent consensus literature, however, the agents in this problem must reach agreement while accounting for the uncertainties in their respective probabilities, which are formulated according to generally non-Gaussian distributions. The first part of this paper considers the problem in which the agents seek agreement to the centralized Bayesian estimate of the probabilities, which is accomplished using consensus on hyperparameters. The second part shows that the new hyperparameter consensus methodology can ensure convergence to the centralized estimate even while measurements of a static process are occurring concurrently with the consensus algorithm. A machine repair example is used to illustrate the advantages of hyperparameter consensus over conventional consensus approaches.

This paper addresses the problem of information consensus in a team of networked agents with uncertain local estimates described by parameterized distributions in the exponential family. In particular, the method utilizes the concepts of pseudo-measurements and conjugacy of probability distributions to achieve a steady-state estimate consistent with a Bayesian fusion of each agent's local knowledge, without requiring complex channel filters or being limited to normally distributed uncertainties. It is shown that this algorithm, termed hyperparameter consensus, is adaptable to any local uncertainty distribution in the exponential family, and will converge to a Bayesian fusion of local estimates over arbitrary communication networks so long as they are known and strongly connected.