Sources of Meaning for Holistic SSA: Emergent Recognition

Gorman, J.

AMOS: Advanced Maui Optical and Space Surveillance Technologies Conference, Maui, Hawaii (September 2010)

Decision-makers are confronted with are confronted with deluge of data describing dynamic conditions in the space enterprise. A Holistic Space Situation Awareness (HSSA) environment considers the space enterprise as a whole and provides comprehensive knowledge of conditions that enable effective and efficient command decisions. Particularly challenging is the need of HSSA to support decision-makers confronted with unexpected and unprecedented situations.

Semantics, significance, and impact are important components of meaning in HSSA. Coordinated semantics ensures accurate exchange of data and information within the space enterprise. Varied terminology and differing conceptual models are semantic barriers to connecting information. Daily, hundreds of events occur that may require a response. HSSA must indentify the events that combine to threaten operations. Finally, HSSA must support command decision making by operationalizing information about the space enterprise and situations that threaten operations. This paper focuses on the problem of detecting significant but unprecedented threats.

The community of space faring nations has grown in recent years and demonstrated capabilities that could threaten space assets and even introduce the possibility of space warfare. A key technical challenge to providing HSSA is the acquisition of broad-range of models for space threat characterization. Clearly, the number and diversity of possible threat patterns is too great for a human expert to enumerate. Emergent learning is an approach that synergistically combines human expertise and machine learning to describe and recognize a broad range of possible threat patterns. Complex concepts emerge from the combination of simpler ingredients.

This paper identifies semantics, significance, and impact as important components of meaning in an HSSA environment. The challenges detecting unknown, unexpected, and unexpected threats are discussed. Emergent learning and Intelligent Reasoning Fusion concepts are introduced as technologies capable of detecting new, unique, and novel threats.

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