Wide-area video exploitation (WAVE) joint data management (JDM) for layered sensing
Erik Blasch, Guna S. Seetharaman, Stephen Russell · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Emerging technologies of high performance computing facilitate increased data collection for wide area sensing; however, joint data management concepts of operations (CONOPs) are important to fully realize system-level performance. Joint data management (JDM) includes the hardware (e.g. sensors/targets), software (e.g. processing/algorithms), entities (e.g. service-based collections), and operations (scenario-based environments) of data exchange that enable persistent surveillance in the context of a layered sensing or data-to-decision (D2D) information fusion enterprise. Key attributes of an information fusion enterprise system require pragmatic assessment of data and information management, distributed communications, knowledge representation as well as a sensor mix, algorithm choice, life-cycle data management, and human-systems interaction. In this paper, we explore the various issues surrounding Wide-Area Video Exploitation (WAVE) in a layered-sensing environment to include improvements in Joint Data Management such as (1) data collection, construction, and transformation, (2) feature generation, extraction and selection, and (3) information evaluation, presentation, and dissemination. Throughout the paper, we seek to describe the current technology, research directions, and metrics that instantiate a realizable JDM product. We develop the methods for joint data management for structured and unstructured WAVE data in the context of decision making. Discerning accurate track and identification target information from WAVE JDM provides a moving intelligence (MOVINT) capability.