Detecting Aircraft With a Low-Resolution Infrared Sensor

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.