Hierarchical data fusion architecture for autonomous systems

Abstract

Autonomy is becoming a key issue concerning unmanned vehicles nowadays. An effective functioning of an unmanned system requests the processing of a large amount of data coming from sensors, onboard databases, etc. Therefore, data fusion technology is a key technology used in autonomous systems. In order to systemise such data processing in autonomous systems, special so-called data fusion architectures are used (e.g. JDL, Waterfall, Boyd). However, some of those solutions have many restrictions. A goal of this study is to present a novel hierarchical data fusion architecture that can be used in autonomous systems. This architecture consists of five basic layers: identification of parameters, state identification, object type identification, situation identification, and task implementation identification. The proposed architecture has some advantages in comparison to those that are already in use. The author considers that the presented architecture has good visibility; intuitive understanding; the possibility of deep feedback usage; and good potential for automatic reconfiguration and self-learning. The developed data fusion architecture can be used for building complex data fusion systems on board of autonomous systems, of a group of unmanned vehicles, and even of systems of a higher hierarchy.