Spatial statistical research in Command and Control (C2) at The Ohio State University is currently focused on the estimation and prediction of danger. To facilitate this task, researchers began by modeling the battlespace, using the hierarchical design illustrated on the Spatial-Temporal page. With this hierarchy in place, the types of available data and then the nature of danger was examined. Simultaneously, an object-oriented battle-simulation program was developed to provide test data for analysis.

This research is supported by the Computational Decision Making Program of the Office of Naval Research.

In the research presented on the Geopolitical Tendencies page, we solve an applied-probability problem of relevance to the intelligence community. Understanding and quantifying the behavioral psychology of enemy nations under conflict situations (or perceived conflict situations) can lead to improved tactical counter-measures. A variety of important strategic questions can be answered that can provide extremely useful intelligence; for example, when threatened, does the enemy tend to retreat and defend, or aggressively counter-attack? Does the enemy attack in isolated pockets, or in a more uniform manner?

In this research, we look to answer these questions in specific situations where intelligence data give the positions and readiness-states of hostile mobile launcher systems. The location and potential threat of mobile launcher systems can vary significantly under different readiness states. By smoothing the spatial point process of mobile launchers (at a given snapshot in time), we obtain intensity maps that quantify the potential threat the launchers imply.

This work was supported by SPAWAR, San Diego.

Almost every aspect of Command and Control (C2) deals with making decisions in the presence of uncertainty. The uncertainty may come from noisy data or, indeed, regions of the battlespace where there are no data at all. It is this latter aspect, namely, lack of knowledge, that requires the most care. Statistical models that account for noisy data are well accepted by the science and engineering community, but the full quantification of uncertainty due to lack of knowledge (caused either by hidden processes or missing data) falls under the purview of statistical modeling.

It is typically the case that the battle commander has a different "aperture" than a platoon commander. The battle commander needs more global, aggregated information, whereas the platoon commander is often making decisions based on local, more focused information. Our emphasis is on making maps that are accurate and easily interpretable at different resolutions/apertures. The intent is not to decide which apertures belong to which level of command, but rather to develop a methodology that makes change-of-aperture possible.

This research was supported by the Probability and Statistics Program of the Office of Naval Research.

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Last modified: Monday, December 10, 2007 11:55 AM.