KEYWORDS: Data modeling, Systems modeling, Information fusion, Data fusion, Sensors, Data processing, Cognitive modeling, Analytical research, Process modeling, Mathematical modeling
Even though the definition of the Joint Director of Laboratories (JDL) "fusion levels" were established in 1987,
published 1991, revised in 1999 and 2004, the meaning, effects, control and optimization of interactions among the
fusion levels have not as yet been fully explored and understood. Specifically, this is apparent from the abstract JDL
definitions of "Levels 2/3 Fusion" - situation and threat assessment (SA/TA), which involve deriving relations among
entities, e.g., the aggregation of object states (i.e., classification and location) in SA, while TA uses SA products to
estimate/predict the impact of actions/interactions effects on situations taken by the participant entities involved. Given
all the existing knowledge in the information fusion and human factors literature, (both prior to and after the introduction
of "fusion levels" in 1987) there are still open questions remaining in regard to implementation of knowledge
representation and reasoning methods under uncertainty to afford SA/TA. Therefore, to promote exchange of ideas and
to illuminate the historical, current and future issues associated with Levels 2/3 implementations, leading experts were
invited to present their respective views on various facets of this complex problem. This paper is a retrospective
annotated view of the invited panel discussion organized by Ivan Kadar (first author), supported by John Salerno, in
order to provide both a historical perspective of the evolution of the state-of-the-art (SOA) in higher-level "Levels 2/3"
information fusion implementations by looking back over the past ten or more years (before JDL), and based upon the
lessons learned to forecast where focus should be placed to further enhance and advance the SOA by addressing key
issues and challenges. In order to convey the panel discussion to audiences not present at the panel, annotated position
papers summarizing the panel presentation are included.
KEYWORDS: Sensors, Information fusion, Weapons, Data fusion, Data processing, Data acquisition, Target detection, Performance modeling, Inspection, Optimization (mathematics)
Resource management (or process refinement) is critical for information fusion operations in that users, sensors, and
platforms need to be informed, based on mission needs, on how to collect, process, and exploit data. To meet these
growing concerns, a panel session was conducted at the International Society of Information Fusion Conference in 2006
to discuss the various issues surrounding the interaction of Resource Management with Level 2/3 Situation and Threat
Assessment. This paper briefly consolidates the discussion of the invited panel panelists. The common themes include:
(1) Addressing the user in system management, sensor control, and knowledge based information collection
(2) Determining a standard set of fusion metrics for optimization and evaluation based on the application
(3) Allowing dynamic and adaptive updating to deliver timely information needs and information rates
(4) Optimizing the joint objective functions at all information fusion levels based on decision-theoretic analysis
(5) Providing constraints from distributed resource mission planning and scheduling; and
(6) Defining L2/3 situation entity definitions for knowledge discovery, modeling, and information projection
KEYWORDS: Data modeling, Process modeling, Systems modeling, Information fusion, Sensors, Knowledge discovery, Error analysis, Data fusion, Cognitive modeling, Process control
Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e. classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) user-fusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies to knowledge representation and reasoning strategies for situation assessment.
This paper describes the application of sequence learning to the domain of terrorist group actions. The goal is to make accurate predictions of future events based on learning from past history. The past history of the group is represented as a sequence of events. Well-established sequence learning approaches are used to generate temporal rules from the event sequence. In order to represent all the possible events involving a terrorist group activities, an event taxonomy has been created that organizes the events into a hierarchical structure. The event taxonomy is applied when events are extracted, and the hierarchical form of the taxonomy is especially useful when only scant information is available about an event. The taxonomy can also be used to generate temporal rules at various levels of abstraction. The generated temporal rules are used to generate predictions that can be compared to actual events for evaluation. The approach was tested on events collected for a four-year period from relevant newspaper articles and other open-source literature. Temporal rules were generated based on the first half of the data, and predictions were generated for the second half of the data. Evaluation yielded a high hit rate and a moderate false-alarm rate.
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