Proceedings Article | 6 August 2002
Proc. SPIE. 4718, Targets and Backgrounds VIII: Characterization and Representation
KEYWORDS: Visualization, Sensors, Bidirectional reflectance transmission function, Fluorescence correlation spectroscopy, Computer aided design, Infrared signatures, Thermal modeling, Electro optical modeling, Systems modeling, Prototyping
There is a push in the Army to develop lighter vehicles that can get to remote parts of the world quickly. This objective force is not some new vehicle, but a whole new way of fighting wars. The Future Combat System (FCS), as it is called, has an extremely aggressive timeline and must rely on modeling and simulation to aid in defining the goals, optimizing the design and materials, and testing the performance of the various FCS systems concepts. While virtual prototyping for vehicles (both military and commercial) has been around as a concept for well over a decade and its use is promoted heavily in tours and in boardrooms, the actual application of virtual protoyping is often limited and when successful has been confined to specific physical engineering areas such as weight, space, stress, mobility, and ergonomics. If FCS is to succeed in its acquisition schedule, virtual prototyping will have to be relied on heavily and its application expanded. Signature management is an example of an area that would benefit greatly from virtual prototyping tools. However, there are several obstacles to achieving this goal. To rigorously analyze a vehicle's IR and visual signatures extensively in several different environments over different weather and seasonal conditions could result millions of potentially unique signatures to evaluate. In addition, there is no real agreement on what evaluate means or even what value is used to represent signature; Delta T( degree(s)C), Probability of Detection? What the user really wants to know is: how do I make my system survivable? This paper attempts to describe and then bound the problem and describe how the Army is attempting to deal with some of these issues in a holistic manner using SMART (Simulation and Modeling for Acquisition, Requirements, and Training) principles.
