Detection and classification of poorly known aircraft with a low-resolution infrared sensor

Existing computer simulations of aircraft infrared signature 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 can not be singly simulated. In this paper, we focus on low resolution infrared sensors and we propose a methodological approach for predicting simulated infrared signature dispersion of poorly known aircraft, and performing aircraft detection and classification on the resulting set of low resolution infrared images. It is based on a Quasi-Monte Carlo survey of the code output dispersion, on a new detection test taking advantage of level sets estimation, and on a maximum likelihood classification taking advantage of Bayesian dense deformable template models estimation.