Data fusion: a conceptual approach to level 2 fusion (situational assessment)

Stephen C. Stubberud; Peter J. Shea; Dale M. Klamer

doi:10.1117/12.484887

25 August 2003 Data fusion: a conceptual approach to level 2 fusion (situational assessment)

Stephen C. Stubberud, Peter J. Shea, Dale M. Klamer

Proceedings Volume 5096, Signal Processing, Sensor Fusion, and Target Recognition XII; (2003) https://doi.org/10.1117/12.484887
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Level 2 fusion is defined as situation awareness. Unfortunately, that is the point where the agreement on Level 2 fusion ends. The distinctions between the boundaries between Levels 1, 2, and 3 are not clearly defined. As a result, these disputes tend to cloud the discussion on the required functionality required of a Level 2 tracking system. Our approach to develop a system that solves a perceived Level 2 problem has three basic tenets: define the problem, develop the concept of the fusion architecture, and define the object state. These tenets provide the foundation to outline and explain the conceptual approach to a Level 2 problem. Each step from the problem fundamentals to the state definition used in the formulation of algorithmic approaches are presented. The discussion begins with a summary of the military problem, which can be considered situation assessment, of multiple levels of unit aggregation to determine force composition, current capabilities, and posture. The problem consists of fusion Level 1 information, incorporating doctrine and other knowledge base information to form a coherent scene of what exists in the field that can then be used as a component of intent analysis. The development of the problem model leads to the development of a Fusion architecture approach. The approach mirrors one of the standard approaches of Level 1 fusion: detection, prediction, association, hypothesis generation and management, and update. Unlike the Level 1 problem, these implementation steps will not become a rehash of the Kalman filter or similar approaches. Instead, the architecture permits a composite set of approaches including symbolic methodologies. The problem definition and the architecture lead to the development of the system state which represents the internal composition of the units and their aggregates. From this point, the discussion concludes with a short summary of potential algorithms proposed for implementation.

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Stephen C. Stubberud, Peter J. Shea, and Dale M. Klamer "Data fusion: a conceptual approach to level 2 fusion (situational assessment)", Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); https://doi.org/10.1117/12.484887

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