We present a novel technique for the modeling of near field optical devices. The key features of this technique are the accuracy of the finite difference time domain method, the advantages of a scattered field formulation, and the direct use the complx permittivity of metals at the frequency of interest.
Thermal IR signatures of buried land mines are affected by various environmental conditions as well as the mine's composition, size and burial geometry. In this work we present quantitative relations for the effect of those factors on the signature's peak contrast and apparent diameter. We begin with a review of the relevant phenomena and the underlying physics. A three-dimensional simulation tool developed by the authors is used to simulate signatures for the case of a static water distribution. We discuss efforts to validate the model using experimental data collected at Fort A.P. Hill, VA. Using this simulation tool a variety of factors are considered, including soil water content, soil sand content, wind speed, mine diameter and mine burial depth.
Many aspects of a buried mine's thermal IR signature can be predicted through physical models, and insight provided by such models can lead to better detection. Several techniques for exploiting this information are described. The first approach involves ML estimation of model parameters and followed by classification of those parameters. We show that this approach is related to an approximate evaluation of an integral over the parameters that arises in a Bayesian formulation. This technique is compared with a generalized likelihood ratio test (GLRT) and with computationally efficient, model-free approaches, in which soil temperature data are classified directly. The benefit of using the temporal information is also investigated. Algorithm performance is illustrated using broadband IR imagery of buried mines acquired over a 24 hour period. It is found that the detection performance at a suitably selected time is comparable to the performance achieved by processing all times. The performance of the GLRT, for which detection is based only on the residual error, is inferior to a classifier using the parameters.
Predicting the thermal signature of a buried land mine requires modeling the complicated inhomogeneous environment and the structurally complex mine. It is useful, both in checking such models and in making rough calculations of expected signatures, to have an accurate, easily computed solution for a relatively simple geometry. In this paper, a reference solution is presented for the integral equation that governs the temperature distribution. Our solution procedure uses the method of weighted residuals. The problem comprises a homogeneous cylindrical body (the mine model) buried in an infinite homogeneous half space (the soil model) with a planar interface. Using periodic boundary conditions in time at the planar interface, the temperature distribution in the lower-half space is expanded in a Fourier series. A volume integral equation for the Fourier series coefficients is obtained via Green's second identity. The Green's function for the Fourier coefficients is derived and reduced to a computationally efficient form. The integral equation is reduced to a matrix equation, which is then solved for the unknown temperature distribution. The integral equation solution is compared with a finite element model.
It has long been recognized that surface-laid land mines and other man-made objects tend to have different polarization characteristics than natural materials. This fact has been used to advantage in a number of mine detecting sensors developed over the last two decades. In this work we present the theoretical basis for this polarization dependence. The theory of scattering from randomly rough surfaces is employed to develop a model for scattering and emission from mines and natural surfaces. The emissivity seen by both polarized and unpolarized sensors is studied for smooth and rough surfaces. The polarized and unpolarized emissivities of rough surfaces are modeled using the solution of the reciprocal active scattering problem via the second order small perturbation method/small slope approximation(SPM/SSA). The theory is used to determine the most suitable angle for passive polarimetric IR detection of surface mines.
KEYWORDS: Mining, Thermal modeling, Solar radiation models, Atmospheric modeling, Cameras, Chemical elements, Finite element methods, 3D modeling, Sensors, Astatine
3D thermal and radiometric models have been developed to study the passive IR signature of a land mine buried under a rough soil surface. A finite element model is used to describe the thermal phenomena, including temporal variations, the spatial structure of the signature, and environmental effects. The Crank-Nicholson algorithm is used for time-stepping the simulation. The mine and the surroundings are approximated by pentahedral elements having linear interpolation functions. The FEM grid for the soil includes a random rough surface having a normal probability density and specified covariance function. The mine is modeled as a homogeneous body of deterministic shape having the thermal properties of TNT. Natural solar insolation and the effects of convective heat transfer are represented by linearized boundary conditions. The behavior over a periodic diurnal cycle is studied by running the simulation to steady state. Finite element solutions for the thermal emissions are combined with reflected radiometric components to predict the signatures seen by an IR camera. Numerical simulations are presented for a representative target, a 25 cm anti-tank mine simulant developed by the US Army. The temporal evolution of the temperature distribution and IR signature are presented for both smooth and rough surfaces.
We describe sensor-based and signal-processing-based techniques for improving the detection of buried land mines in thermal IR imagery. Results of experimental studies using MWIR and LWIR imaging systems are reported. Thermal clutter due to surface reflected sunlight and skylight are investigated and shown to be the dominant clutter component for both MWIR and LWIR imagery collected during daylight hours. A sensor-based clutter reduction technique, spectral differencing, was considered and found to provide some benefit. The temporal evolution of thermal signatures was investigated. The imagery are found to have near-Gaussian statistics, and therefore the deflection coefficient is a valid measure of detectability. The deflection coefficient for some buried mines was found to improve with time after sunset. In addition, the LWIR band appears to offer some advantages in detection. Clutter mitigation via signal processing is also explored using an 'estimator-classifier' technique in which target-related parameters are estimated from the data and detected with a classifier. The theoretical basis of the method is discussed. MWIR and LWIR imagery are used to illustrate both the sensor-based and signal-processing-based techniques.
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