Proceedings Article | 31 July 2002
KEYWORDS: Analytical research, Defense and security, Kinematics, Data fusion, Weapons, Data modeling, Neural networks, Feature extraction, Sensors, Metals
Situation analysis is defined as a process, the examination of a situation, its elements, and their relations, to provide and maintain a product, i.e., a state of situation awareness, for the decision maker. Data fusion is a key enabler to meeting the demanding requirements of military situation analysis support systems. According to the data fusion model maintained by the Joint Directors of Laboratories' Data Fusion Group, impact assessment estimates the effects on situations of planned or estimated/predicted actions by the participants, including interactions between action plans of multiple players. In this framework, the appraisal of actual or potential threats is a necessary capability for impact assessment. This paper reviews and discusses in details the fundamental concepts of threat analysis. In particular, threat analysis generally attempts to compute some threat value, for the individual tracks, that estimates the degree of severity with which engagement events will potentially occur. Presenting relevant tracks to the decision maker in some threat list, sorted from the most threatening to the least, is clearly in-line with the cognitive demands associated with threat evaluation. A key parameter in many threat value evaluation techniques is the Closest Point of Approach (CPA). Along this line of thought, threatening tracks are often prioritized based upon which ones will reach their CPA first. Hence, the Time-to-CPA (TCPA), i.e., the time it will take for a track to reach its CPA, is also a key factor. Unfortunately, a typical assumption for the computation of the CPA/TCPA parameters is that the track velocity will remain constant. When a track is maneuvering, the CPA/TCPA values will change accordingly. These changes will in turn impact the threat value computations and, ultimately, the resulting threat list. This is clearly undesirable from a command decision-making perspective. In this regard, the paper briefly discusses threat value stabilization approaches based on neural networks and other mathematical techniques.
