Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain

摘要

A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers C. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted.