"Optical (IR/VIS/UV) multispectral vehicle coating/pattern optimizer," F.J. Iannarilli, Proc. IRIS Symp. on Camouflage, Concealment, and Deception, (1997).

Aerodyne Research, Inc., with substantial support from Lockheed and Boeing Helicopters, has developed a computer-aided design tool known as the "Paint Mapping(c) Optimizer" or "PMO". PMO has evolved in capability over the last 7 years, and has been extensively applied in both fixed-wing and rotary-wing contrast reduction design efforts. PMO determines the optimal assignment of coatings from a user-defined multispectral "palette" to vehicle surface elements (i.e., "facets"). Various measures of design merit can be invoked, presently including minimization of gross contrast, luminance and chromaticity (for spatially unresolved viewing), pattern complexity, and dollar cost of applied materials. Specification of multiple contrast reduction objectives, spanning a desired range of variability in environmental, vehicle operating, and threat viewing conditions ensures that the resulting coating scheme will be a robust multiscenario solution.

To guide its optimization process, PMO utilizes the signature predictions of first principles codes such as GSL and SPIRITS, and can readily utilize similar codes such as GTSIG or PRISM. This flexibility enables it to be employed for virtually any vehicle type for which signatures can be computed, e.g., ground, maritime, and air vehicles. The PMO technology has been competitively selected via contract award from AATD/Ft. Eustis. The effort underway will expand PMO's optimization measures of merit to include spatially resolved viewing, specifically manipulation of camouflage pattern for minimization of human visual conspicuity as well as detectibility against autonomous seekers. It will also be interfaced to the GTSIMS and PRISM models.

PMO's utility extends beyond the determination of a coating/pattern scheme for a particular vehicle. It enables rapid quantification of trade sensitivities between performance, range of scenario variability, coating technologies, scheme complexity, and dollar cost. Thus, PMO can determine the payoff of employing various collections of coating properties, and guide the downselection of unpromising technology concepts.