Distributed Situational Awareness and Control
Seng Keat Gan, Zhe Xu, Salah Sukkarieh · Encyclopedia of Aerospace Engineering · 2016
Abstract In this chapter, we describe the benefits of applying distributed unmanned aerial vehicle (UAV) teams to situational awareness problems. The situational awareness problem is decomposed into two components: decentralized data fusion and team decision making to maximize information gain. The decentralized data fusion problem aims to build and share a target state estimate (or belief) across the UAV team based on each UAV's observations. This chapter describes three common target state estimate representations: the Kalman and information filter, certainty grid representations, and particle filters. For each representation, we outline how it may be applied in a distributed situational awareness problem. The team decision‐making problem selects the best team actions to minimize uncertainty (or maximize information) in the target state estimate. In this chapter, we outline the mathematical formulation of the general team decision‐making problem and how it can be simplified to yield tractable, online solutions.