Data Pedigree and Strategies for Dynamic Level-One Sensor Data Fusion

• Main Authors: Ceruti, Marion G, Powers, Brenda J, Wright, Tedd L, McGirr, Scott C
• Format: Report
• Language: English
• Subjects: Cybernetics; DATA FUSION; DECISION AIDS; DECISION SUPPORT; DETECTORS; DISTRIBUTED SENSING; Information Science; KNOWLEDGE MANAGEMENT; LEVEL-ONE DATA FUSION; METADATA; Miscellaneous Detection and Detectors; ONTOLOGY; PEDIGREE METADATA; SENSORS; SYMPOSIA; TRACKING

Knowledge Management for Distributed-Tracking (KMDT) is a U.S. Naval research and development project to improve military-communications and information functions in the battle space. These functions include command, control, data fusion, and decision support. It features a scenario for modeling and simulation that shows how knowledge-management technologies, such as ontologies and intelligent agents can improve battle-space awareness and the decision making process in command centers with respect to distributed tracking and threat identification of targets. Data on cross lines of bearings can be acquired from sensors using a secure network. These data and their associated pedigree metadata from multiple platforms in the battle space can be fused to reduce the uncertainty in platform detection, localization, classification and identification (level-one data-fusion object refinement). The pedigree metadata can affect how data are used in fusion tasks. Presented at the International Conference on Information Fusion (9th) held in Florence, Italy on 10-13 July 2006.