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Conference Program Committee
Session Chairs
IntroductionContext is present in all aspects of processing and interpreting information—situation, data, text, imagery, target tracking/identification, web-analytics, and intelligence systems outputs; that is, in all aspects/levels of information fusion (IF). Context is a multi-faceted entity and can represent a setting for the assessment/interpretation of an event, scene, presence, situation, condition, constraint, influence, and many other entities clearly scenario/application dependent. There is context within context. Furthermore, context is not a static entity and can change over time (e.g., operating conditions, environment, geography, weather, seasons, roads, traffic, attitudes, behavior, preferences), affecting the performance of a given application if not managed and taken into account. Therefore, it is important to incorporate contextual information at the outset in all IF levels and associated systems designs in order to enhance the performance of the overall IF system and the on-going application. For a given application, contextual information represents prior domain knowledge about the setting of the scenario/process to commence. The contextual knowledge can be acquired from prior (historical) experience, provided by external sources (e.g., the user), learned from process experience (e.g., context awareness, prediction and search), and it can be updated/corrected if changes are detected (e.g., by machine learning). For example, one can describe at least five contextual categories in tracking applications: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis; (2) environment-to-hardware processing for sensor management; (3) known distribution of entities for situation/threat assessment; (4) historical traffic behavior for situation awareness patterns of life (POL); and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large amount of data available for a priori knowledge target-tracking algorithm construction, implementation, and application. The objective of this panel was to bring to the attention of the fusion community the importance of the application of contextual knowledge to enhance IF by highlighting issues, illustrating potential approaches, and addressing challenges. A number of invited experts discussed the challenges of the fusion process as well as the research addressing these challenges. The panelists illustrated parts of the aforementioned areas over different applications and addressed all levels of information fusion. Conceptual and real-world related examples associated with the use of context to enhance IF were used by the panel to highlight impending issues and challenges. Ivan Kadar Erik Blasch Chee-Yee Chong |
