Some key electro-optical measurements required to characterize an aircraft plume for automated recognition are shown, as well as some aspects of the processing and use of these measurements. Plume measurements with Short Wavelength Infrared (1.1 – 2.5 um), Mid-Wavelength Infrared (2.5 – 7 um) and Long Wavelength Infrared (7 – 15 um) cameras are presented, as well as spectroradiometer measurements covering the whole Mid-Wavelength, Long Wavelength and upper part of the Short Wavelength Infrared bands. The two limiting factors for the detection of the plume, i.e. the atmospheric transmission bands and the plume emission bands, are discussed, and it is shown how a micro turbine engine can assist in aircraft plume studies. One such a study, regarding the differentiation between an aircraft plume and a blackbody emitter using subbands in the Mid-Wavelength Infrared, is presented. The factors influencing aircraft plume emission are discussed, and the measurements required to characterize an aircraft plume for the purpose of constructing a mathematical plume model are indicated. Since the required measurements are prescribed by the plume model requirements, a brief overview of the plume model, that can be used to simulate the results of the plume’s emission under different conditions and observation configurations, is given. Such a model can be used to test the robustness of algorithms, like the mentioned subband method, for identifying aircraft plumes. Such a model furthermore enables the simulation of measurements that would be obtained by an electro-optical system, like an infrared seekerhead of a missile, of a plume for the purpose of algorithm training under various simulated environmental conditions.
A radiance inversion technique, in which in-flight aircraft plume radiance recordings are exploited to construct a three dimensional (3D) radiance model of the plume, is presented. The recordings were done with a mid-wave infrared (3 – 6 μm) camera at different altitudes. The algebraic formulation of this inversion technique, also known as an emission-absorption technique, is stated for the ideal case of spectral radiance measurements of a high spatial sampling resolution over the plume area, as would be obtained from a hyperspectral imager. The non-ideal case of having only broad-band (mid-wave) image measurements and only one spectral measurement of the plume, is then investigated. It is shown that from this incomplete information set, an effective spectral absorption coefficient can be calculated for which the associated plume spectral transmittance and spectral emissivity calculations exhibit the correct qualitative behaviour. It is also shown that, by using this effective absorption coefficient, an optimization procedure can be used to determine the temperatures and/or spectral radiance values within the plume. This optimization procedure consists of minimizing the difference between the observed line of- sight (LOS) radiance in the image (i.e. a pixel radiance value in the image) and its theoretical projected radiance. After the temperature values within the plume were determined, the observer LOS radiance is parameterized so that it can be described for an arbitrary angle with respect to the main axis of the plume. The inferred temperature, spectral transmittance and spectral emissivity are then used in calculating the expected spectral radiance at this arbitrary angle. The spectrally integrated/mid-wave broad-band radiances and intensities for aspect angles other than those used during the inversion process, are then calculated and compared with actual measurements in order to determine the adequacy of this model for incorporation into existing infrared imaging system simulation software used in the training of infrared seekerhead missiles.
Thermal crossover is the phenomenon where the infrared signatures of two different objects in a scene are indistinguishable. A prediction method was developed where a series of infrared images is used as the basis to predict thermal crossover under different climatic conditions. Image recordings are made over the full diurnal cycle, for a fixed scene. We then develop a theoretical thermal model, describing dynamic temporal behaviour. Using the recorded images, the model parameters required to describe the temporal behaviour of the observed scene, are determined. The model, with the appropriate model parameters, is then used to create a new image sequence, predicting the scene appearance under different climatic conditions. The new image sequence is used to predict thermal crossover under the new set of climatic conditions. The paper closes with conclusions and recommendations for future work.