Statistical Estimation of Aircraft Infrared Signature Dispersion

Knowledge of aircraft InfraRed Signature (IRS) is indispensable for assessing their detection probability, and thus their survivability in an hostile environment. By signature, we mean all the quantities for predicting the signal that would be observed by an optronic sensor when the aircraft is in its surroundings. For many reasons, the experimental approach is generally not feasible to evaluate the IRS dispersion. Computer programs, which enable to evaluate the IRS of aircraft and backgrounds, are therefore extremly valuable tools. Existing computer simulations of aircraft IRS [1] do not account for the dispersion induced by uncertainty on input data, such as aircraft aspect angles and meteorological conditions. As a result, they are of little use to estimate the detection performance of IR optronic systems: in that case, the scenario encompasses a lot of possible situations, that must indeed be addressed, but can not be singly simulated. In this paper, a three-step methodological approach for predicting simulated IRS dispersion of poorly known aircraft is proposed. The first step is a sensitivity analysis, which identifies inputs that have negligible influence on the IRS and can be set at a constant value. The second step consists in a Quasi-Monte Carlo survey of the code output dispersion. In the last step, a metamodel of the IRS simulation code is constructed. This method is illustrated in a typical scenario, namely a daylight air-to-ground full-frontal attack by a generic combat aircraft flying at low altitude, and gives a satisfactory estimation of the infrared signature dispersion.