Context-enhanced Information Fusion Techniques

Contextual Information (CI) can be understood as the information that “surrounds” an observable of interest. Even if not directly part of the problem variables being estimated by the system, CI can influence their state or even the sensing and estimation processes. Therefore, understanding and exploiting CI can improve the performance of information fusion algorithms and automatic systems in general that have to deal with varying operating conditions. There is a growing interest for this promising research using context such as static or dynamic structures, and be represented in many different ways such as maps, knowledge-bases, ontologies, etc. It can constitute a powerful tool to favor adaptability and boost system performance. Applications discussed include: context-aided surveillance systems (security/defense), traffic control, autonomous navigation, cyber security, ambient intelligence, ambient assistance, etc. The lecture surveys existing approaches for context-enhanced information fusion, covering the design and development of information fusion solutions integrating sensory data with contextual knowledge, and identifying trends such as physics-based and human-derived information fusion (PHIF), physics-augmented machine learning (PAML), and digital twins.

L. Snidaro, J. Garcia Herrero, J. Llinas, E. Blasch (eds.), Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge, Springer, 2016.