title: Detecting aircraft with a low-resolution infrared sensor.
abstract: Existing computer simulations of aircraft infrared signature (IRS) do not account for 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 this case, the scenario encompasses a lot of possible situations that must be indeed addressed, but cannot be singly simulated. In this paper, we focus on low-resolution infrared sensors and we propose a methodological approach for predicting simulated IRS dispersion of poorly known aircraft and performing aircraft detection on the resulting set of low-resolution infrared images. It is based on a sensitivity analysis, which identifies inputs that have negligible influence on the computed IRS and can be set at a constant value, on a quasi-Monte Carlo survey of the code output dispersion, and on a new detection test taking advantage of level sets estimation. This method is illustrated in a typical scenario, i.e., a daylight air-to-ground full-frontal attack by a generic combat aircraft flying at low altitude, over a database of 90,000 simulated aircraft images. Assuming a white noise or a fractional Brownian background model, detection performances are very promising.
authors.affiliation: Télécom Sud Paris, RST, Evry, France.
authors.forename: Jérémie
authors.initials: J
authors.lastname: Jakubowicz
authors.forename: Sidonie
authors.initials: S
authors.lastname: Lefebvre
authors.forename: Florian
authors.initials: F
authors.lastname: Maire
authors.forename: Eric
authors.initials: E
authors.lastname: Moulines