RESUMEN
Black carbon aerosol emissions are recognized as contributors to global warming and air pollution. There remains, however, a lack of techniques to remotely measure black carbon aerosol particles with high range and time resolution. This article presents a direct and contact-free remote technique to estimate the black carbon aerosol number and mass concentration at a few meters from the emission source. This is done using the Colibri instrument based on a novel technique, referred to here as Picosecond Short-Range Elastic Backscatter Lidar (PSR-EBL). To address the complexity of retrieving lidar products at short measurement ranges, we apply a forward inversion method featuring radiometric lidar calibration. Our method is based on an extension of a well-established light-scattering model, the Rayleigh-Debye-Gans for Fractal-Aggregates (RDG-FA) theory, which computes an analytical expression of lidar parameters. These parameters are the backscattering cross-sections and the lidar ratio for black carbon fractal aggregates. Using a small-scale Jet A-1 kerosene pool fire, we demonstrate the ability of the technique to quantify the aerosol number and mass concentration with centimetre range-resolution and millisecond time-resolution.
RESUMEN
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.
RESUMEN
One of the major limitations to the use of infrared focal plane arrays (IRFPAs) in stationary Fourier transform spectrometers (FTSs) comes from the spatial inhomogeneities of the pixel responses, where the inhomogeneities of the cut-off wavenumbers of the pixels can prevail. The hypothesis commonly assumed for FTSs that all the pixels are equivalent is thus inaccurate and results in a degradation of the estimated spectrum, even far from the cut-off wavenumbers. However, if the individual spectral responses of the pixels are measured beforehand, this a priori information can be used in the inversion process to produce reliable spectra. Thus, spatial inhomogeneities are not an obstacle for the use of infrared stationary FTS. This result is illustrated in this paper by numerical simulations, based on a realistic description of an IRFPA.
RESUMEN
We address an original statistical method for unsupervised identification and concentration estimation of spectrally interfering gas components of unknown nature and number. We show that such spectral unmixing can be efficiently achieved using information criteria derived from the Minimum Description Length (MDL) principle, outperforming standard information criteria such as AICc or BIC. In the context of spectroscopic applications, we also show that the most efficient MDL technique implemented shows good robustness to experimental artifacts.
RESUMEN
A design of a miniaturized stationary Fourier transform IR spectrometer has been developed that produces a two-dimensional interferogram. The latter is disturbed by effects like parasitic interferences or disparities in the cutoff wavelength of the pixels. Thus, a simple Fourier transform cannot be used to estimate the spectrum of the scene. However, as these defects are deterministic, they can be measured and taken into account by inversion methods. A regularization term can also be added. The first experimental results prove the efficiency of this processing methodology.
RESUMEN
We propose a modeling of the aggregation processes of optical properties and temperature over the heterogeneous landscape in the infrared domain (3-14 microm). The main objectives of the modeling are to understand how these parameters aggregate and to study their links at different spatial scales. As the landscape is described at each scale by its radiative parameters, general equations linking the radiative parameters at a given high spatial scale to those at a rough scale are proposed. Then these equations are applied to several synthetic landscapes. An analysis based on a design of experiments is conducted to point out the influence of each of the input factors. The results show the importance of the intrinsic parameters (reflectance, emissivity, and surface temperature) of each surface element and also the directional and spectral behaviors of the aggregated parameters.
RESUMEN
The background scene generator MATISSE, whose main functionality is to generate natural background radiance images, makes use of the so-called Correlated K (CK) model. It necessitates either loading or computing thousands of CK coefficients for each atmospheric profile. When the CK coefficients cannot be loaded, the computation time becomes prohibitive. The idea developed in this paper is to substitute fast approximate models for the exact CK generator; using the latter, a representative set of numerical examples is built and used to train linear or nonlinear regression models. The resulting models enable an accurate CK coefficient computation for all the profiles of an image in a reasonable time.
