Space System Threat Mission Impact Assessment

The space domain presents a deluge of disparate data overloading analysts to the point where critical information can be missed. The abundance of data heightens the appeal of information fusion solutions. In general, sensor fusion approaches can be viewed as bottom-up and driven by technical capabilities. Often neglected are the information needs that can help human operators optimize performance. We assert improved fusion techniques can arise by implementing top-down processes derived from operator information needs, task demands, and work environment context. Numerous cognitive engineering methods are available to construct such processes. Thus we posit a bidirectional  approach (Space SA via Bidirectional User-centered Fusion - SpaceBUF), is needed to achieve higher-level fusion and support the human operator with decision tasks. The fusion process is ultimately concerned with sense making. Collectible data must be transformed into usable information, targeted to characteristics that play a role in decision making for mission capability. The efficacy of the fusion algorithms are ultimately determined by the system''''''''s ability to support the decision makers by providing information in a timely, usable fashion that addresses their information needs and improves performance. BENEFIT: The purpose is to use this framework to connect operator derived information needs with the ability to process data to fulfill those needs. Our key innovation augments the usually bottom-up data driven information fusion process, and imposes top-down techniques emphasizing the needs of the human operator. This is achieved with cognitive engineering methods, relying heavily on WDA, a multi-stage analytic framework that efficiently leads to the elucidation of user information needs. Thus, fusion algorithms serve to map the available data from the environment to critical aspects of the human operator''''''''s decision-making space. Using cognitive engineering methods in a top-down process, we strive to make attractor states to which the bottom-up data aggregation can project to meet. This approach enables information aggregation in a way that fulfills user needs and is not simply driven by technological capabilities or the intuition of experts.