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Command and Control (C2) in a military setting can be epitomized in battles‐of‐old when commanders would seek high ground to gain superior spatial‐temporal information; from this vantage point, decisions were made and relayed to units in the field. Although the fundamentals remain, technology has changed the practice of C2; for example, enemy units may be observed remotely, with instruments of varying positional accuracy. A basic problem in C2 is the ability to track an enemy object in the battlespace and to forecast its future position; the (extended) Kalman filter provides a straightforward solution. The problem changes fundamentally if one assumes that the moving object is headed for an (unknown) location, or waypoint. This article is concerned with the new problem of estimation of such a waypoint, for which we use Bayesian statistical prediction. The computational burden is greater than an ad hoc regression‐based estimate, which we also develop, but the Bayesian approach has a big advantage in that it yields both a predictor and a measure of its variability. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004 相似文献
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The problem of detecting a minefield in the presence of clutter can be abstracted to that of detecting a spatial pattern within a set of point locations. The point locations are superpositions of several patterns, one of which corresponds to mines. In contrast to previous articles that take a formal, model‐based approach, this article proposes a statistical methodology that is distinctly exploratory. Each point location is considered separately, and its contributions to a global measure of spatial distances between locations are featured. Different patterns and unusual points can be more easily identified on the new scale. Both minefield data and simulated point patterns demonstrate the power of the method. © 2001 John Wiley & Sons, Inc., Naval Research Logistics 48:333–347, 2001 相似文献
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