Modern target recognition systems suffer from the lack of human-like abilities to understand the visual scene, detect, unambiguously identify and recognize objects. As result, the target recognition systems become dysfunctional if target doesn't demonstrate remarkably distinctive and contrast features that allow for unambiguous separation from background and identification upon such features. This is somewhat similar to visual systems of primitive animals like frogs, which can separate and recognize only moving objects. However, human vision unambiguously separates any object from its background. Human vision combines a rough but wide peripheral, and narrow but precise foveal systems with visual intelligence that utilize both scene and object contexts and resolve ambiguity and uncertainty in the visual information. Perceptual grouping is one of the most important processes in human vision, and it binds visual information into meaningful patterns and structures. Unlike the traditional computer vision models, biologically-inspired Network-Symbolic models convert image information into an "understandable" Network-Symbolic format, which is similar to relational knowledge models. The equivalent of interaction between peripheral and foveal systems in the network-symbolic system is achieved via interaction between Visual and Object Buffers and the top-level system of Visual Intelligence. This interaction provides recursive rough context identification of regions of interest in the visual scene and their analysis in the object buffer for precise and unambiguous separation of the object from background/clutter with following recognition of the target.

Maximum diversity in sample data is required in order to ensure that results from such are representative of the entire domain. In cases where the generation of data is computationally expensive, such as image synthesis, the number of samples should be kept to a minimum with a higher density in regions of transition with respect to image variation. Our objective is to synthesize a set of hyperspectral images to evaluate the performance of ATRs. We use the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, an image synthesizing software, to generate the images. The nature of a synthesized image is determined by numerous input parameters to DIRSIG. It is required that the resulting image set be diverse with respect to the degree of difficulty for the ATRs under test. We model each synthesized image as a function of the input parameters to DIRSIG, each parameter being a possible source of variation in the image. We compute a Complexity Measure (CM) for each image that represents the degree of difficulty for an ATR. A gradient based sampling scheme is infeasible to determine the regions of transitions in the CM in the multiparameter space because of the computational cost of synthesizing each image. We thus present a sampling algorithm based on an active walker model, in which the step size is adapted based on the distribution of the CM values from the already synthesized images. We sample a variety of multi-dimensional functions with this algorithm, and confirm the improved reconstruction accuracy from samples obtained using it compared to even and random sampling. When applied to sampling the CM multi-parameter space, our adaptive sampling algorithm produces a more diverse image set with respect to degree of difficulty than the random and even sampling schemes.

In this paper, we present a study of target recognition based on curvature measures. The goal of this work is to examine identification algorithms based on contour and surface curvature estimations. F. Costa and R. M. Cesar have developed fractal and curvature analysis methods for the contours in 2D images. In particular, they have introduced the notion of the curvegram of a contour. This provides a visual representation of how the curvature changes with scale. We propose an extension to target recognition based on curvature descriptors but involving the whole target set not just the boundary.

This paper examines the attribution of data fields required to generate high resolution soil profiles for support of Computational Test Bed (CTB) used for countermine research. The countermine computational test bed is designed to realistically simulate the geo-environment to support the evaluation of sensors used to locate unexploded ordnance. The goal of the CTB is to derive expected moisture, chemical compounds, and measure heat migration over time, from which we expect to optimize sensor performance. Several tests areas were considered for the collection of soils data to populate the CTB. Collection of bulk soil properties has inherent spatial resolution limits. Novel techniques are therefore required to populate a high resolution model. This paper presents correlations between spatial variability in texture as related to hydraulic permeability and heat transfer properties of the soil. The extracted physical properties are used to exercise models providing a signature of subsurface media and support the simulation of detection by various sensors of buried and surface ordnance.

The surface magnetic anomaly observed in UXO clearance is mainly dipolar and, consequently, the dipole is the only magnetic moment regularly recovered in UXO applications. The dipole moment contains information about intensity of magnetization but lacks information about shape. In contrast, higher-order moments, such as quadrupole and octupole, encode asymmetry properties of the magnetization distribution within the buried targets. In order to improve our understanding of magnetization distribution within UXO and non-UXO objects and its potential utility in UXO clearance, we present a 3D numerical modeling study for highly susceptible metallic objects. The basis for the modeling is the solution of a nonlinear integral equation describing magnetization within isolated objects. A solution for magnetization distribution then allows us to compute magnetic moments of the object, analyze their relationships, and provide a depiction of the surface anomaly produced by different moments within the object. Our modeling results show significant high-order moments for more asymmetric objects situated at depths typical of UXO burial, and suggest that the increased relative contribution to magnetic gradient data from these higher-order moments may provide a practical tool for improved UXO discrimination.

With the changing nature of international security it is of interest to the military to use remote sensors to detect and classify people as potential threats. We chose a millimeter wave (MMW) radar as our sensor to collect data on single and small groups of people that were either walking or running to determine how easily they could be detected. This work was done to support the concept of using Ka-band radar to detect people from an airborne platform. Fully-polarimetric Ka-Band radar data was collected of people walking and running at various orientations with respect to the radar. Micro-Doppler analysis reveals Doppler oscillations with time that are characteristic of people at all orientations measured.

A ray trace model was developed to simulate atmospheric effects upon short range imaging of a scene for a narrow frequency band centered at 650 GHz. The frequency chosen is in the vicinity of a terahertz frequency band atmospheric window. We describe the assumptions that went into this model, including the statistics for the density distribution of atmospheric water vapor, which is one of the main molecular absorbers in this spectral region. We also summarize the results of a measurement program in which point sensors (with a 20 Hz sampling rate) measured wind-advected water vapor density. We examine the validity of the atmospheric absorption statistics assumed in our imaging model through a spectral analysis of the temporal series for absorption coefficient derived from the water vapor density data.

In this paper we present the results of our efforts to develop a digital hyperspectral signature model for terrain features. The infrared signature model development was conducted in conjunction with a parallel spectral phenomenology research program devoted to understanding and exploiting spectral data for landmine detection. That effort identified a need for a dynamic hyperspectral signature model to support target detection algorithm development. One requirement of that model was the need for a model of spectral infrared signatures of various terrain features (e.g., soils) that accounts for the impact of various dynamical environmental factors and processes. A discussion of the terrain modeling approach, the underlying analytical basis, and results from the model computations will be presented.

Optical imaging, including infrared imaging, generally has many important applications, both civilian and military. In recent years, technological advances have made multi- and hyperspectral imaging a viable technology in many demanding military application areas. The aim of the CEPA JP 8.10 program has been to evaluate the potential benefit of spectral imaging techniques in tactical military applications. This unclassified executive summary describes the activities in the program and outlines some of the results. More specific results are given in classified reports and presentations. The JP 8.10 program started in March 2002 and ended in February 2005. The participating nations were France, Germany, Italy, Netherlands, Norway, Sweden and United-Kingdom, each with a contribution of 2 man-years per year. Essential objectives of the program were to: 1) analyze the available spectral information in the optronic landscape from visible to infrared; 2) analyze the operational utility of multi- and hyperspectral imaging for detection, recognition and identification of targets, including low-signature targets; 3) identify applications where spectral imaging can provide a strong gain in performance; 4) propose technical recommendations of future spectral imaging systems and critical components. Finally, a stated objective of the JP 8.10 program is to "ensure the proper link with the image processing community". The presentation is organized as follows. In a first step, the two trials (Pirrene and Kvarn) are presented including a summary of the acquired optical properties of the different landscape materials and of the spectral images. Then, a phenomenology study is conducted analyzing the spectral behavior of the optical properties, understanding the signal at the sensor and, by processing spectroradiometric measurements evaluating the potential to discriminate spectral signatures. Cameo-Sim simulation software is presented including first validation results and the generation of spectral synthetic images. Results obtained on measured and synthetic images are shown and discussed with reference to two main classes of image processing tasks: anomaly detection and signature based target detection. Furthermore, preliminary works on band selection are also presented which aim to optimize the spectral configuration of an image sensor. Finally, the main conclusions of the WEAG program CEPA JP8.10 are given.

The Irma synthetic signature prediction code is being developed to facilitate the research and development of multi-sensor systems. Irma was one of the first high resolution, physics-based Infrared (IR) target and background signature models to be developed for tactical weapon applications. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser (or active) channel. This two-channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model, which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR/MMW model, Irma 4.0. In 2000, Irma version 5.0 was released which encompassed several upgrades to both the physical models and software. Circular polarization was added to the passive channel, and a Doppler capability was added to the active MMW channel. In 2002, the multibounce technique was added to the Irma passive channel. In the ladar channel, a user-friendly Ladar Sensor Assistant (LSA) was incorporated which provides capability and flexibility for sensor modeling. Irma 5.0 runs on several platforms including Windows, Linux, Solaris, and SGI Irix. Irma is currently used to support a number of civilian and military applications. The Irma user base includes over 130 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry. In 2005, Irma version 5.1 was released to the community. In addition to upgrading the Ladar channel code to an object oriented language (C++) and providing a new graphical user interface to construct scenes, this new release significantly improves the modeling of the ladar channel and includes polarization effects, time jittering, speckle effect, and atmospheric turbulence. More importantly, the Munitions Directorate has funded three field tests to verify and validate the re-engineered ladar channel. Each of the field tests was comprehensive and included one month of sensor characterization and a week of data collection. After each field test, the analysis included comparisons of Irma predicted signatures with measured signatures, and if necessary, refining the model to produce realistic imagery. This paper will focus on two areas of the Irma 5.1 development effort: report on the analysis results of the validation and verification of the Irma 5.1 ladar channel, and the software development plan and validation efforts of the Irma passive channel. As scheduled, the Irma passive code is being re-engineered using object oriented language (C++), and field data collection is being conducted to validate the re-engineered passive code. This software upgrade will remove many constraints and limitations of the legacy code including limits on image size and facet counts. The field test to validate the passive channel is expected to be complete in the second quarter of 2006.

We compute joint AM-FM models that characterize infrared targets and backgrounds in the modulation domain. We consider spatially localized structures within an IR image as sums of nonstationary, quasi-sinusoidal functions admitting locally narrowband amplitude and frequency modulations. By quantitatively estimating the modulations that dominate the signal spectrum on a spatially local basis, we obtain a new modulation domain feature vector that can augment the more traditional pixel domain, Fourier spectrum, and multispectral color features that have been used in IR target detection and tracking systems for a long time. Our preliminary studies, based primarily on midwave and longwave missile approach sequences, suggest that IR targets and backgrounds do typically possess sufficient spatially local modulated structure (i.e., texture) for modulation domain techniques to be meaningfully applied. We also present qualitative results strongly indicating that the modulation domain feature vector is a powerful tool for discriminating infrared targets and backgrounds.

Modeling infrared (IR) synthetic scenes typically involves a different paradigm than modeling vehicles and other targets. Ground vehicles are modeled using meshed geometric representations that allow the 3D heat equation to be solved simultaneously for every element in the mesh. This includes calculation of 3D heat conduction, convective heat transfer including plume impingement, and radiation exchange between parts of the vehicle. Due to computational limitations it is not possible to model IR synthetic scenes using this same approach. For most synthetic scenes it is not practical to create geometric representations of each blade of grass or of every leaf. Due to the differences in modeling paradigms it becomes problematic to couple the thermal solutions directly so that the vehicle and terrain interact. For this reason, radiation exchange between the vehicle and the terrain or the effects of plume impingement on the terrain are not often modeled within a synthetic scene. To address this limitation, MuSES (the Multi-Service Electro-optic Signature code), an infrared signature prediction program developed for modeling ground vehicles and other man-made targets, has been integrated into CameoSim, a broadband scene simulation software system that produces high resolution synthetic imagery of natural terrestrial scenes. To achieve the desired level of thermal interaction, a geometric description of the terrain surrounding the target is exported from CameoSim into MuSES; MuSES then calculates the temperature of both the target and the supplied terrain. To minimize artifacts between the temperature prediction of the terrain local to and distant from the target, MuSES terrain thermal models can be specified for use in the greater CameoSim scene. The resulting software tool is capable of modeling large scale IR synthetic scenes that include full thermal interaction between the target and the terrain in an area local to the target.

CHORALE (simulated Optronic Acoustic Radar battlefield) is used by the French DGA/DET (Directorate for Evaluation of the French Ministry of Defense) to perform multi-sensors simulations. CHORALE enables the user to create virtual and realistic multi spectral 3D scenes, and generate the physical signal received by a sensor, typically an IR sensor. To evaluate their efficiency in visible and infrared wavelength, simulation tools, that give a good representation of physical phenomena, are used. This article describes the elements used to prepare data (3D database, materials, scenario, ...) for the simulation, and the set of tools (SE-FAST-IR), used in CHORALE for the Real Time simulation in the infrared spectrum. SE-FAST-IR package allows the compilation and visualization of 3D databases for infrared simulations. It enables one to visualize complex and large databases for a wide set of real and pseudo-real time applications. SE-FAST-IR is based on the physical model of the Non Real Time tool of CHORALE workshop. It automatically computes radiance textures, Open GL light source and fog-law parameters for predefined thermal and atmospheric conditions, specified by the user.

Continued interest in temporal variations of optical turbulence argues for the development of a model to characterize turbulence conditions for ground-to-ground, ground-to-air, and air-to-ground observation/propagation scenarios. Standard vertical profile models of the refractive index structure parameter (C²_n) are available in segmented form, but are here combined to produce a single mean model of vertical structure for near noon daytime conditions that combines surface layer, boundary layer, and upper tropospheric effects. This model eliminates the discontinuities and ambiguities present in previous results. The temporal evolution of turbulence is also discussed, and several of the challenges to modelling C²_n at a single level and developing temporally evolving vertical profile are addressed. The temporal evolution appears best handled via a surface energy budget model, which when coupled with an evolving profile model would provide greatly improved capabilities for simulating turbulence.

To date, physically accurate and reasonably fast background generation in a marine environment has been an elusive objective. The pursuit has been plagued by slow radiative transfer codes and sea BRDF (bidirectional reflectance distribution functions) implementations that traded accuracy for speed. Recent developments in both these fields have put this goal into our grasp. In this paper, we show that an accurate and fast sea BRDF model can be implemented into modern radiative transfer codes. In this instance, it is integrated in a widely used code, MODTRAN 4, to produce marine environment backgrounds with an acceptable computation time and less tradeoff in accuracy.

This paper presents the MATISSE-v1.4 code whose main functionality is to compute spectral or integrated natural background radiance images. The spectral bandwidth extends from 765 to 3300 cm^-1 (3 to 13 μm) with a 5 cm^-1 resolution. Natural backgrounds include the atmosphere, low and high altitude clouds, sea and land. The most particular functionality of the code is to take into account atmospheric spatial variability quantities (temperatures, mixing ratio, etc) along each line of sight of the image. In addition to image generation capacity, the code computes atmospheric radiance and transmission along a line of sight with the same spectral characteristics as in imaging mode. In this case atmospheric refraction effects and radiation from high or low altitude clouds can be taken into account. A high spectral resolution mode is also available to propagate radiation from a high temperature medium in the same atmospheric state as that used for the image generation. Finally, an Application Programming Interface (API) is included to facilitate its use in conjunction with external codes. This paper describes the range of functionalities of MATISSE-v1.4 whose release is planned for April 2006. Future developments are also presented.

A physics based 3D simulation of sea surfaces is presented. The simulation is suitable for the pre-calculation of detector images for an IR camera. Synthetic views of a maritime scenario are calculated in the MWIR and LWIR spectral bands and the images are compared with data collected in a field trial. In our computer simulation the basic sea surface geometry is modeled by a composition of smooth wind driven gravity waves. Sea surface animation is introduced by time dependent control of the basic statistics. Choppy waves are included into the model to improve the realism of the rough sea. To predict the view of a thermal camera the sea surface radiance must be calculated. This is done with respect to the emitted sea surface radiance and the reflected sky radiance, using either MODTRAN or a semi-empirical model. Slope-shadowing of the sea surface waves is considered, which strongly influences the IR appearance of the sea surface near the horizon. MWIR and LWIR simulations are shown of sun glint as well as of whitecaps which depend upon wind velocity. For validation purposes appropriate data sets (images and meteorological data) were selected from field measurements. A simple maritime scenario including a floating foreground object has been prepared and views of two different thermal imagers, similar to those used in the field trials, have been simulated. The validation is done by visual inspection of measured and simulated images and in addition by numerical comparison based on image statistics. The results of the comparison are presented. For an accurate reflectance calculation it is necessary to consider the maritime sky. The model is improved by inclusion of a static two-dimensional cloud layer. The cloud distribution adjusted to measured data with respect, e.g. to power spectral density and temperature distribution.

The naval ship infrared signature model and naval threat countermeasure simulator (ShipIR/NTCS) developed by W.R. Davis Engineering Ltd has undergone extensive validation since its adoption as a NATO-standard, and has been accredited by the US Navy for Live Fire Test and Evaluation of the DDG class warship, Preliminary Design of the DD(X) destroyer, and Contract Design and Live Fire Test and Evaluation of DD(X). Validation has played a key role in the model development by assessing current accuracy, identifying key areas of improvement, and tracking achievements made by each new release. This paper describes some of the recent improvements in full-ship infrared (IR) signature measurement and model prediction based on the measurements and predictions of an unclassified Canadian research vessel (CFAV Quest). The results show how some of the more recent trial parameters: radiosonde input, ship surface optical properties, atmosphere-scattered solar irradiation, and large-scale Reynolds Number; have affected our model predictions and accuracy.

A program has been developed that divide apparent areas in a RadThermIR simulation model and IR registration of a platform in different temperature intervals. It offers the possibility to choose any number of temperature intervals as well as the interval sizes. The calculation also provides statistical parameters such as mean temperature, median temperature and standard deviation. In an IR registration, the object can automatically be identified. For complex objects and rare views a manual method of indicating the objects can be used. The program can be used during the design for IR signature control and verification once the design is completed. The program is demonstrated by using a simulation model and IR images.

A new experiment has been conducted with a composite propellant rocket motor in order to get two kinds of information: first one is concerning the physical and optical properties of aluminates particles that are emitted in the plume exhaust; second one is concerning the spectral and spatial repartition of radiance in the plume infrared images. The size distribution and the optical properties of particles are used as entry data for flowfield computation, whereas infrared spectra and images are used to evaluate the capacities of the simulation tools to produce infrared signature data.

Infrared synthetic imagery simulators are commonly used for validation of infrared imaging terminal guidance missile performances. A high level of confidence for infrared synthetic imagery simulation is needed. The prediction fiability depends on radiance model quality and input parameters knowledge. An infrared radiance contrast prediction model was developed at ONERA to study environmental and target/background characteristics effects on contrasts in infrared scene. The aim of this study is to compare the prediction robustness in middlewave and longwave infrared spectral bands (MWIR, LWIR), and, later, to estimate input parameters uncertainties on the prediction quality. This paper presents the validation methodology and validation study results. A specific validation criterion is used to evaluate the model ability to predict the presence or the lack of contrast between two objects separated by a line (segment). Simulated radiance contrasts are compared to measured contrasts on the PIRRENE test site located 30 km south west of Toulouse, France. Model validation needs a large number of conditions to cover the application domain. The specified conditions are: 2 climatological conditions (summer, winter), 2 meteorological conditions (clear sky, cloudy sky) and 28 segments combining 7 materials and 3 geometries (horizontal/horizontal, vertical/horizontal and vertical/vertical). MWIR and LWIR radiance contrasts are simulated for each condition on complete diurnal cycle with 15-minute sampling.

To perform multi-sensors simulations, the French DGA/DET (Directorate for Technical Evaluation of the French Ministry of Defense) uses CHORALE (simulated Optronic Acoustic Radar battlefield). CHORALE enables the user to create virtual and realistic multi spectral 3D scenes, and generate the physical signal received by a sensor, typically an IR sensor. This article presents how the expertise is made to evaluate smart ammunition with laser guidance in a virtual battlefield with the environment CHORALE and the workshop AMOCO. The scene includes background, targets, a laser to designate and ammunition. The laser source is reflected by a target in the battlefield and the laser receiver is linked with ballistics model and guidance model via a simulation framework. Each tool is explained to understand the physics phenomena in the scene to take into account atmospheric transmission, radiative parameters of objects and counter-measure devices. Then numeric models are described as the different ballistics models 3 DOF or 6 DOF, sensor model. The step of ballistics calculation gives the cadence of the global simulation through the simulation framework. The 4 quadrants sensor provides gap between the center of optical way and the barycentre of the spot on the sensitive surface computed by a spot weighted method. These data are provided to the guidance and ballistics model to calculate a new position and a new view of the scene with the designated target in the field view. Finally, this paper explains some results of the evaluation compared with the true behavior after tests on proving ground. Then future evolutions are presented to perform similar evaluation with other intelligent ammunition in a real-time model.

Accurate thermal modeling requires verification and validation of the model and software being used. For basic evaluation of thermal prediction models and software tools, a generic model - CUBI was build. The model was designed to have simple geometry yet, consisted of similar characteristics as of a ground vehicle. The model was equipped with thermocouples for measuring its temperature variations and was placed in a typical desert environment for field testing. The experimental setup also included a meteorological station. The data collected was used for the thermal behavior analysis of the generic model and for comparison with the thermal calculations predictions. Comparison of the results shows sufficient compliance but yet reviles some issues in the modeling that should be addressed.

Accurate information on the distribution of sensible and latent heat fluxes as well as soil moisture is critical for evaluation of background characteristics. Since these fluxes are subject to rapid changes in time and space, it is nearly impossible to determine their spatial and temporal distributions over large areas from ground measurements alone. Therefore, prediction from remote sensing images is very attractive as it enables extensive area coverage and a high repetition rate. In this study, the Surface Energy Balance Algorithm for Land as implemented at New Mexico Tech (SEBAL^NM) is used to estimate sensible and latent heat fluxes in the White Volta Basin of Ghana, West Africa. The objectives are (i) to demonstrate a SEBAL^NM application in a part of the world were ground measurements are very scarce and (ii) to compare evapotranspiration (ET) maps obtained from Landsat and MODIS imagery, respectively. The results of this study demonstrate that SEBAL^NM can be applied for mapping sensible and latent heat fluxes as well as soil moisture over areas where few or no ground measurements are available using common satellite products (Landsat and MODIS).

When designing camouflage it is important to understand how the human visual system processes the information to discriminate the target from the background scene. A vision model has been developed to compare two images and detect differences in local contrast in each spatial frequency channel. Observer experiments are being undertaken to validate this vision model so that the model can be used to quantify the relative significance of different factors affecting target conspicuity. Synthetic imagery can be used to design improved camouflage systems. The vision model is being used to compare different synthetic images to understand what features in the image are important to reproduce accurately and to identify the optimum way to render synthetic imagery for camouflage effectiveness assessment. This paper will describe the vision model and summarise the results obtained from the initial validation tests. The paper will also show how the model is being used to compare different synthetic images and discuss future work plans.

Commercially available fluid-dynamics solvers, such as FLUENT, are being developed to analyze and design missiles of increasing complexity. These robust solvers can be further adapted to predict spectral radiation directly. While retaining the capability to predict signatures from underexpanded axisymmetric rocket exhaust typical of most rockets, this new capability can be extended to include subsonic missiles, such as sea-skimmers, ballistic missiles operating in near-vacuum conditions and side-discharging rockets for manual and semi-automatic command missile guidance. The preliminary results presented in this study suggest that when combined with available atmospheric models, these comprehensive codes can be used to develop improved threat detection and missile guidance optics.

This paper presents an experiment carried out in Toulouse in 2004. This campaign aims to create a specific library which will give us simultaneously information in three domains: a list of the main materials present in the city, the optical properties of each of them (spectral and directional) and their spatial variability in a given class. The spectral domain covers the entire optical domain from the visible to the Long Wave InfraRed range. Measurements have been carried out in the visible and near infrared spectral region (400-2500 nm) with an ASD spectroradiometer at a 20 cm resolution for outdoors measurements, and with a goniometer for laboratory ones at the same spatial resolution. A database of about 550 individual spectra has been created. These spectra could be divided into 4 classical urban classes like road (red asphalt, tar), pavement (red asphalt, tar), square (granite slab) and wall (brick, concrete). In addition to these "in situ" experiments, the bi-directional behaviours of urban material samples have been studied in laboratory with the Onera goniometer. Two material types have been distinguished: flat materials, which is isotropic, and textured materials, whose study is more complex. Whereas road and sidewalk materials are quite lambertian with a slight backscattering effect typical of rough surfaces, square materials like granite or concrete present a specular peak at large zenith angle. A specific study on tiles demonstrates their important anisotropic directional properties. In the infrared domain (3μm - 14μm), a SOC 400 spectroradiometer was used at a 1.27cm spatial resolution. A database of about 100 individual spectra has been created. These spectra could be divided into four classical urban classes like road (red asphalt, tar), pavement (red asphalt, tar), square (granite slab) and wall (bricks, painted walls). In each spectral domain, three variability types are considered: a physical variability which is intrinsic to the material, a contextual variability depending on the material use and a theoretical variability which is the one observed inside a chosen class.

Raytheon Missile Systems (RMS) conducts ongoing research into an extensible general-purpose missile simulation framework. Recent research has focused on the development of a generic seeker simulation framework to support effective seeker design and verification. This paper presents advances in the techniques used to integrate the scene generation capability into genSim, the extensible off-the-shelf missile simulation implementation developed at Raytheon Missile Systems. The most recent field of inquiry is the robust scene generation of synthetic infrared signatures (including both land-vehicular and maritime targets) and is an element of the network-centric Future Combat Systems (FCS) program. In response to our DoD customer's transition to a capabilities-based technology acquisition and development approach, a discussion of scene generation simulation integration is of interest because of the increased use of simulation in the validation of weapon system performance.

SEDRIS that stands for Synthetic Environment Data Representation and Interchange Specification is a DoD/DMSO initiative in order to federate and make interoperable 3D mocks up in the frame of virtual reality and simulation. This paper shows an original application of SEDRIS concept for research physical multi sensors simulation, when SEDRIS is more classically known for training simulation. CHORALE (simulated Optronic Acoustic Radar battlefield) is used by the French DGA/DCE (Directorate for Test and Evaluation of the French Ministry of Defense) to perform multi-sensors simulations. CHORALE enables the user to create virtual and realistic multi spectral 3D scenes, and generate the physical signal received by a sensor, typically an IR sensor. In the scope of this CHORALE workshop, French DGA has decided to introduce a SEDRIS based new 3D terrain modeling tool that enables to create automatically 3D databases, directly usable by the physical sensor simulation CHORALE renderers. This AGETIM tool turns geographical source data (including GIS facilities) into meshed geometry enhanced with the sensor physical extensions, fitted to the ray tracing rendering of CHORALE, both for the infrared, electromagnetic and acoustic spectrum. The basic idea is to enhance directly the 2D source level with the physical data, rather than enhancing the 3D meshed level, which is more efficient (rapid database generation) and more reliable (can be generated many times, changing some parameters only). The paper concludes with the last current evolution of AGETIM in the scope mission rehearsal for urban war using sensors. This evolution includes indoor modeling for automatic generation of inner parts of buildings.

The Standoff Aerosol Active Signature Testbed (SAAST) is the aerosol range within the MIT Lincoln Laboratory's Optical System Test Facility (OSTF). Ladar and Lidar are promising tools for precise target acquisition, identification, and ranging. Solid rocket effluent has a strong Lidar signature. Currently, calculations of the Lidar signature from effluent are in disagreement from measurements. This discrepancy can be addressed through relatively inexpensive laboratory measurements. The SAAST is specifically designed for measuring the polarization-dependent optical scattering cross sections of laboratory-generated particulate samples at multiple wavelengths and angles. Measurements made at oblique angles are highly sensitive to particle morphology, including complex index of refraction and sample shape distribution. With existing hardware it is possible to re-aerosolize previously collected effluent samples and, with online and offline diagnostics, ensure that these samples closely represent those found in situ. Through comparison of calculations and measurements at multiple angles it is possible to create a realistic model of solid rocket effluent that can be used to extrapolate to a variety of conditions. The SAAST has recently undergone a dramatic upgrade, improving sensitivity, flexibility, sample generation, sample verification, and level of automation. Several measurements have been made of terrestrial dust and other samples.