Defence R&D Canada is developing a Collaborative Knowledge Exploitation Framework (CKEF) to support the analysts in efficiently managing and exploiting relevant knowledge assets to achieve maritime domain awareness in joint operations centres of the Canadian Forces. While developing the CKEF, anomaly detection has been clearly recognized as an important aspect requiring R&D. An activity has thus been undertaken to implement, within the CKEF, a proof-of-concept prototype of a rule-based expert system to support the analysts regarding this aspect. This expert system has to perform automated reasoning and output recommendations (or alerts) about maritime anomalies, thereby supporting the identification of vessels of interest and threat analysis. The system must contribute to a lower false alarm rate and a better probability of detection in drawing operator's attention to vessels worthy of their attention. It must provide explanations as to why the vessels may be of interest, with links to resources that help the operators dig deeper. Mechanisms are necessary for the analysts to fine tune the system, and for the knowledge engineer to maintain the knowledge base as the expertise of the operators evolves. This paper portrays the anomaly detection prototype, and describes the knowledge acquisition and elicitation session conducted to capture the know-how of the experts, the formal knowledge representation enablers and the ontology required for aspects of the maritime domain that are relevant to anomaly detection, vessels of interest, and threat analysis, the prototype high-level design and implementation on the service-oriented architecture of the CKEF, and other findings and results of this ongoing activity.

Date Published: 15 April 2008
PDF: 14 pages
Proc. SPIE 6945, Optics and Photonics in Global Homeland Security IV, 69450W (15 April 2008); doi: 10.1117/12.776230

Published in SPIE Proceedings Vol. 6945:
Optics and Photonics in Global Homeland Security IV
Craig S. Halvorson; Daniel Lehrfeld; Theodore T. Saito, Editor(s)