Search, Classification and Attack Decisions for Cooperative Wide Area Search Munitions

Abstract

There are currently several wide area search munitions in the research and development phase within the Department of Defense. While the work on the airframes, sensors, target recognition algorithms and navigation schemes is promising, there are insufficient analytical tools for evaluating the effectiveness of these concept munitions. Simulation can be used effectively for this purpose, but analytical results are necessary for validating the simulations and facilitating the design trades early in the development process. Recent research into cooperative behavior for autonomous munitions has further highlighted the importance of fundamental analysis to steer the direction of this new research venture. This paper presents extensions to some classic work in the area of search and detection. The unique aspect of the munition problem is that a search agent is lost whenever an attack is executed. This significantly impacts the overall effectiveness in a multi-target/false target environment. While the analytic development here will concentrate on the single munition case, extensions to the multi-munition will be discussed to include the potential benefit from cooperative classification and engagement.

The views expressed in this article are those of the authors and do not reflect the official policy of the U.S. Air Force, Department of Defense, or the U.S. Government.