RESEARCH BY SUBJECT AREA
Theory
1. Simultaneous Tracking and Identification
2. User Refinement in Information Fusion
3. High-Level Information Fusion
4. Information Fusion Performance Evaluation
5. Image Fusion
Applications
6. Robotics (UAVs)
7. Cyber
8. Space Situational Awareness
9. Medical
10. Sensor Specific
1. Simultaneous Tracking and Identification (STID) includes elements of moving object labeling from classification information, kinematic categorization, and set-based analysis of signature data towards identification (e.g., allegiance). Developments include methods to improve not only the track information (e.g. location, velocity); but utilizing the track information to update the object label (e.g. attribute, type, and category). STID moves beyond approaches for amplitude of classification-aided tracking in that the type, allegiance, and fingerprinting can be determined for target identification for such needs as situation and threat awareness. The ideas where utilize as feature-aided tracking (FAT) Methods developed for both tracking and labeling can be used in sensor management approaches to improve detection, recognition, classification, and identification.
B. Kahler and E. Blasch, “Decision-Level Fusion Performance Improvement from Enhanced HRR Radar Clutter Suppression,” J. of. Advances in Information Fusion, Vol. 6, No. 2, Dec. 2011.
E. Blasch and C Yang, “Ten methods to Fuse GMTI and HRRR Measurements for Joint Tracking and ID,” Int. Conf. on Info Fusion - Fusion 04, July 2004.
E. Blasch, Derivation of a Belief Filter for Simultaneous High Range Resolution Radar Tracking and Identification, Ph.D. Thesis, Wright State University, 1999.
2. User Refinement in Information Fusion focuses on the interaction between users and machines where information is concerned. The appreciation of human contribution to information fusion begins with the system strategists, engineering designers, software implementers, real-time operators and forensic analysts. This has been referred to Level 5 Information Fusion as User refinement and includes human-machine systems (e..g, visualization, reporting) to cognitive computing (e.g., sense-making, analytics).
E. Blasch and S. Plano, “DFIG Level 5 (User Refinement) issues supporting Situational Assessment Reasoning,” Int. Conf. on Info Fusion - Fusion 05, July 2005.
E. P. Blasch and S. Plano, “JDL Level 5 Fusion model ‘user refinement’ issues and applications in group Tracking,” Proc. SPIE, Vol. 4729, 2002.
E. P. Blasch and P. Hanselman, "Information Fusion for Information Superiority," IEEE Nat’l Aerospace and Electronics Conf., Dayton, OH, pg. 290 – 297, 2000.
E. P. Blasch “Assembling a distributed fused Information-based Human-Computer Cognitive Decision Making Tool,” IEEE Aerospace and Electronic Systems Magazine, Vol. 15, No. 5, pp. 11-17, May 2000. (also presented at the International Conference on Information Fusion, 1999)