As part of the survivability engineering process it is necessary to accurately model and visualize the vehicle signatures in multi- or hyperspectral bands of interest. The signature at a given wavelength is a function of the surface optical properties, reflection of the background and, in the thermal region, the emission of thermal radiation. Currently, it is difficult to obtain and utilize background models that are of sufficient fidelity when compared with the vehicle models. In addition, the background models create an additional layer of uncertainty in estimating the vehicles signature. Therefore, to meet exacting rendering requirements we have developed RenderView, which incorporates the full bidirectional reflectance distribution function (BRDF). Instead of using a modeled background we have incorporated a measured calibrated background panoramic image to provide the high fidelity background interaction. Uncertainty in the background signature is reduced to the error in the measurement which is considerably smaller than the uncertainty inherent in a modeled background. RenderView utilizes a number of different descriptions of the BRDF, including the Sandford-Robertson. In addition, it provides complete conservation of energy with off axis sampling. A description of RenderView will be presented along with a methodology developed for collecting background panoramics. Examples of the RenderView output and the background panoramics will be presented along with our approach to handling the solar irradiance problem.

IR synthetic scene fidelity improves with each leap ahead in computing capability. Military training, in particular, is reaping the benefits from each improvement in rendering fidelity and speed. However, in order for these synthetic scenes to be useful for signature virtual prototyping or laboratory observer trials, a particularly challenging aspect still needs to be addressed. Synthetic scenes need to have the ability to include robust physically reasonable active source prediction models for vehicles and to include physically reasonable interaction of vehicles with the terrain. Ground heating from exhaust, radiative heating and reflections between the vehicle and terrain, and tracks left on the terrain are just some examples of desired capabilities. For determining the performance of signature treatments, the effects must be more than artistic renderings of vehicle terrain interaction, but physically representative enough to make engineering determinations. This paper will explore the results of a first phase study to include MuSES targets in an existing IR synthetic scene program and the inclusion of exhaust impingement on the terrain.

Obscurant representation is a key component of ground battlefield simulation, especially in the infrared domain. Obscurant are special counter measures (clouds) classically used to hide armored vehicles and deceive threatens. Obscurants are very difficult to represent especially because of multi diffusion effects of hot particles and smoke, but this representation is very important to quantify the efficiency of the decoy. This article describes a new model being involved in the simulation workshop CHORALE of the French MoD. The simulation workshop CHORALE developed in collaboration with OKTAL SE company is used by government services and industrial companies for weapon system validation and qualification trials in the infrared domain. The main operational reference for CHORALE is the assessment of the infrared guidance system of the Storm Shadow missile French version, called Scalp. This new model, integrated in CHORALE, enables to simulate the emitted radiance and the transmission of any pre computed obscurant cloud in the virtual battlefield. In the modeling step, the cloud is defined by a set of "voxels" (elementary volume elements). Each voxel contains the spectral extinction coefficient and the spectral scattering coefficients. The shape, i.e. the voxels content, is changing with time to convey the dynamic evolution of the obscurant. In the Non Real Time rendering step, primary rays are traced inside the clouds. For each voxel, scattering rays are then traced to their neighboring voxels and the local hot sources. Actually, ray tracing is used to solve the Radiative Transfer Equation. The main advantage is to be able to solve it taking into account the synthetic environment: the local terrain, the target hidden in the cloud, the atmospheric and weather conditions. The main originality is the multithreading ray tracing which enables to tackle huge quantities of rays in complex geometric environment.

The manoeuvreist doctrine requires that commanders have the freedom to move around the battlespace to locations where they can best influence the outcome. To achieve this, commanders must be secure in the knowledge that they can do so undetected. Infrared (IR) sensors, with ever increasing sensitivity, are now well established in the land environment, IR signature management is therefore becoming evermore necessary for battlefield equipment. High-fidelity thermal signature models are available but require high levels of operator expertise, detailed input data, and they are time consuming to run. A simple, easy to use thermal signature model would provide a ready alternative to the more exacting complex models. Whilst not attempting to replace the high-fidelity models for the detailed analysis of thermal signatures, a simple model would have utility as a first filter of trials data and the initial testing of signature reduction concepts. A single temperature difference model of a Main Battle Tank (MBT), incorporating the emissive and reflective components of its thermal signature, is presented. This model is used as the input into a Minimum Resolvable Temperature Difference (MRTD) model for a generic thermal imaging tank sight which is used to predict the detection ranges of the MBT. The reduction in detection range afforded by emissivity variations is calculated and demonstrates the potential benefits and difficulties of this signature management procedure.

A system for reactive control of the thermal infrared (TIR) signature is described. The system is primarily developed for ground vehicles, but can easily be adapted to other platforms. The main parts of the system are a sensor package for background and vehicle signature awareness, actuators for heating/cooling of the vehicle surface and control algorithms to connect the sensor signals with the actuator control. Some of the more important aspects of the development process are discussed, including physical modeling and simulation. Physical modeling has been a useful tool, and the models are also included as part of the control algorithms. Reactive signature control should be regarded as a component in a Defensive Aids Suite (DAS). The crew is provided with the current signature status as well as high-contrast warnings. The signature control system can also be considered as a countermeasure, since it reduces the probability for detection as well as the hit probability for heat-seeking missiles. The system will soon be implemented and tested in a ground technology demonstrator.

An infrared field trial has been conducted by a NATO science panel on IR ship signatures, TG-16. This trial was planned, designed and executed for the expressed purpose of the validation of predictive IR ship signature simulations. The details of the trial were dictated by a thoughtful validation methodology, which exploits the concept of "experimental precision." Two governmental defense laboratories, the Norwegian Defence Research Establishment and the US Naval Research Laboratory have used this trial data to perform a validation analysis on the ShipIR IR signature code. This analysis quantifies prediction accuracy of the current versions of the code and identifies specific portions of the code that need to be upgraded to improve prediction accuracy.

The ship signature model ShipIR/NTCS has been selected as a NATO standard. In 2001 Norway participated in the SIMVEX field trial arranged by NATO in Canada for validation of this model. The measurements were performed on a research vessel under different meteorological conditions, when the ship was sun illuminated and shaded, and also at night. This paper presents spectral results from our high resolution FTIR spectroradiometer, Bomem DA5. Using in-house software that enables correction of non-ideal properties of the spectroradiometer, we obtained improved absolute precision of calibrated spectra. The FTIR results are most interesting for sources with signatures deviating significantly from blackbody functions, like the ship plume, sun illuminated surfaces and sea and sky backgrounds. Ship surface and sea and sky background results have been compared with ShipIR/NTCS predictions. Results from plume measurements have been compared with simulated spectra, using the FASCODE atmospheric model, and we have estimated the plume temperature and the concentration of the most important IR contributing molecules.

Present systems simulate sea surfaces either in the visible or in the IR band. A physics based 3D simulation of sea surfaces for the calculation of images for multiband cameras is presented here. Dynamic sea surfaces, composed of smooth wind-driven gravity waves, are generated by means of time dependent statistical models. In addition, choppy waves are modeled to improve the realism of the rough sea. The appearance of the sea in the visible and thermal bands is modeled. Sea surface radiance in the IR band is calculated with respect to the reflected sky radiance and the emitted sea surface radiance. Sun glint simulations in the visible and IR are presented. Polarization effects were incorporated to enhance the physical realism. As an example for an application a real-time animation of a sea surface with floating foreground objects is shown. The simulated images of the sea surface are in good accordance with real images.

For a variety of training and simulation purposes even photo-realistic synthetic imagery is inadequate because of the impact of subtle effects on the eye and on other sensors. It is essential that the synthetic imagery is a physically accurate representation of the real-world and captures all the inherent variability of different backgrounds. CAMEO-SIM has been developed to meet these requirements. Recent work has improved the atmospheric modelling and thermal shadow simulation. In addition, novel concepts to introduce the three-dimensional spatial and spectral variability required are under consideration. It is essential that the fidelity of the imagery generated is evaluated, to ensure that it is 'fit for purpose'. Therefore a toolset, FIRE, has been developed. This toolset can assess metrics such as 'clutter level' within the image. A range of validation studies have been undertaken throughout the development of CAMEO-SIM. This paper will give an overview of the current capabilities of CAMEO-SIM and describe planned developments. The validation work will be reviewed, especially the recent work on thermal modelling and analysis using FIRE.

In this paper we give a high level discussion outlining methodologies and techniques employed in generating high fidelity terrain and target models. We present the current state of our IR signature development efforts, cover custom tools and algorithms, and discuss future plans. We outline the steps required to derive an IR terrain and target signature models, and provide some details about algorithms developed to classify aerial imagery. In addition, we discuss our tool used to apply IR signature data to tactical vehicle models. We discuss how we process the empirical IR data of target vehicles, apply it to target models, and generate target signature models that correlate with the measured calibrated IR data. The developed characterization databases and target models are used in digital simulations by various customers within the US Army Aviation and Missile Command (AMCOM).

In response to popular demand, The Visual Group has undertaken an effort known as the Portable Source Initiative, a program intended to create cross-platform compatible multi-spectral databases by building a managed source set of data and expending a minimal amount of effort republishing run-time formatted proprietary databases. By building visual and sensor databases using a variety of sources, then feeding all value-added work back into standard, open, widely used source formats, databases can be published from this "refined source data" in a relatively simple, automated, and repeatable fashion. To this end, with the endorsement of the Army's PM Cargo, we have offered a sample set of the source data we are building for the CH-47F TFPS program to the visual simulation industry at large to be republished into runtime formats. The results of this effort were an overwhelming acceptance of the concepts and theories within, and support from both industry and multi-service flight simulation teams.

With the advent of hyperspectral imaging spectrometers comes the need for procedures that detect and interrogate spectral quantities of interest. Such procedures or algorithms play a key role in the dissemination and interpretation of hyperspectral data. Validation of these algorithms involves well-characterized field collection campaigns that can be time and cost prohibitive. Radiometrically, as well as geometrically, correct synthetic imagery offers algorithm developers a surrogate to potentially unattainable field campaigns. The image simulation surrogate must ideally match real world scenes in both spatial and spectral complexity for one to have faith in algorithm performance. To this end, there is a need to develop synthetic scenes, based on real world data, which encompass full 3-dimensional geometric complexities as well as wide-area, spectrally complex backgrounds. Prior work has been done on the inclusion of backgrounds into a synthetic environment, however, this work did not generate wide-area imagery with all the complexities realized in real world data. This paper investigates the generation of a wide area synthetic scene rendered by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. The large area scene or "MegaScene" described in this paper is 0.6 square miles and contains an order of magnitude increase in objects, materials, and spectra, as compared to previously rendered scenes. Hyperspectral analysis using off-the-shelf classification and target detection algorithms was performed on the data to illustrate quantitative and qualitative fidelity.

The Swedish Defence Research Agency (FOI) has performed systematic measurements on a background scene over a period of one year, and established an IR-background database. It has, and will, be used for a wide range of applications and provide a basis for the modeling of IR-background properties of Swedish terrain. The database consists of traditional IR images and hyperspectral IR images, as well as weather data from the measurements. The major part of this paper describes the analysis of the hyperspectral data that was collected with the FTIR based ScanSpec system. In this paper the degree of greybody behavior from objects in the background have been studied in the LWIR and MWIR spectral regions. The analysis has been performed for seven different surfaces of the scene for different times of the day and different days of the year. The paper describes the method of the analysis, some results and attempts to find explanations to the observed phenomena. It is found that the background mainly behaves as greybodies and that the most important reason for deviations is the reflected sunlight. Among the studied surfaces asphalt shows the most obvious deviation from greybody behavior.

The simulation workshop CHORALE developed in collaboration with OKTAL SE company for the French MoD is used by government services and industrial companies for weapon system validation and qualification trials in the infrared domain. The main operational reference for CHORALE is the assessment of the infrared guidance system of the Storm Shadow missile French version, called Scalp. The use of CHORALE workshop is now extended to the acoustic domain. The main objective is the simulation of the detection of moving vehicles in realistic 3D virtual scenes. This article briefly describes the acoustic model in CHORALE. The 3D scene is described by a set of polygons. Each polygon is characterized by its acoustic resistivity or its complex impedance. Sound sources are associated with moving vehicles and are characterized by their spectra and directivities. A microphone sensor is defined by its position, its frequency band and its sensitivity. The purpose of the acoustic simulation is to calculate the incoming acoustic pressure on microphone sensors. CHORALE is based on a generic ray tracing kernel. This kernel possesses original capabilities: computation time is nearly independent on the scene complexity, especially the number of polygons, databases are enhanced with precise physical data, special mechanisms of antialiasing have been developed that enable to manage very accurate details. The ray tracer takes into account the wave geometrical divergence and the atmospheric transmission. The sound wave refraction is simulated and rays cast in the 3D scene are curved according to air temperature gradient. Finally, sound diffraction by edges (hill, wall,...) is also taken into account.

Field tests to compare camouflage patterns rely on collecting data on the preferences of human observers. The director of such tests has been faced with a choice between using pencil-and-paper ballots or using an expensive data collection system based on push buttons wired to personal computers with custom software. In this paper we describe an alternative system that combines the advantages of digital collection with the simplicity of paper ballots. The key ingredients to the system are personal data assistants (PDA's) and database software that runs on a PDA. Specifically, our system makes use of Palm Pilots and the commercial database program thinkDB. Using a stylus, each observer enters his selection of the better camouflage pattern by pushing a radio button on the screen of his Palm Pilot. At the end of the test, the test director uses the Palm HotSync function to transfer the results to a personal computer for analysis.

The Informational Difference (InDiff) is a measure of the difference between two image sets. Previous work has shown that it can be used to explain result of important target recognition experiments involving human observers. In this paper we present the results of investigations on the suitability of using the InDiff as a measure of target conspicuity. First, the InDiff is defined and adapted to measuring the difference between two images, one containing a target on a background and the other - containing only the background. Second, we present results of two experiments involving human observers: In one experiment, gray level images of complex scenes were presented to the observers; the second experiment involved color images. The response times for detecting and of recognizing targets in these images were measured and the InDiff values for the images were calculated. Correlation coefficients of 0.60 - 0.85 were found between the InDiff values and the following quantities: Detection speed in both experiments, recognition speed in the gray-level experiment. Significant relations were found between the probability of correct detection or recognition and a quantity based on the InDiff, in the gray-level experiment. Finally we discuss possible applications of these findings and suggest extension to the formalism.

The camouflage assessment tool LOAT was introduced to the Austrian Air Reconnaissance Center at the end of 1999. This segmentation based and texture oriented method was already presented at AeroSense Conferences. For the new task of assessing video sequences of partly moving objects this single image method seemed to be too time consuming - a different approach relying on former experiences seemed to be feasible. In June 2002 a three nation (Austria, Germany, Switzerland) camouflage-field-campaign "MUSTAFA" was conducted on an Austrian proving-ground; data was recorded for the following expert analysis of the participants. Motion imagery was recorded in visual and thermal infrared bands. The scenario was the approach of an attack-helicopter towards typical military targets which were camouflaged with multi spectral measures. Besides a validation of the LOAT-System by selecting a small sample of the collected video frames a so called "videobase analysis method" is being developed and is scheduled for examining the videos for motion-camouflage-assessment. The "videobase analysis method" (it will become LOMAT-system = low observables motion assessment tool if successful) is based on a very fast video-processing software. The assessment algorithm to be implemented is based on a MDL clustering algorithm in conjunction with a database for numerical inter frame analysis. Features are the results of the clustering process like information content of the data, number of classes found and the like. We consider it a very promising approach for the projected real-time task mainly because there is no calibration process involved. The research done by D2K Solutions Ruppert&Partner OEG, an Austrian spin-off company of Joanneum Research is scheduled to provide the basic tool with end of 2002; the examination of the MUSTAFA data is due until May 2003. The approach of the "videobase-analysis method", the introduction of the MDL based assessment method will be presented together with some preliminary visualizations e.g. screenshots of the work done so far. Samples of the collected motion-image will also be presented.

We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

Infrared cameras are widely used in today's battlefield for surveillance purpose. Because of retroreflection, an incident laser beam entering the camera optics results in a beam reflecting back to the direction of the laser source. An IR detector positioned close to the laser source can then detect the reflected beam. This effect can reveal the location of the cameras and thus increases the risk of covert operations. In the present work, the characteristics of the retroreflection is studied. It is found that the reflection intensity is high when the incident beam enters through the middle part of the lenses while it is low and the beam is diverged when entering through the outer part of the lenses. The reflection is symmetric when the incident beam is normal to the lenses while asymmetric when it is incident with an angle to the lenses. In order to study the potential effects on retroreflection of modified camera optics, IR low index slides (ZnSe and KCl with refractive indices of 2.49 and 1.54, respectively) with different thicknesses (2mm, 4mm and 6mm) are placed in the optical system. The result shows that the focal point of the lenses is changed by the addition of the slide but the optical paths of the reflection remain unchanged. The relationship between the different slides and beam intensity is also studied.

UV-based imaging systems can be used for low-altitude rockets detection or biological agents identification (for instance weapons containing ANTHRAX). Compared to conventional CCD technology, CMOS-based active pixel sensors provide several advantages, including excellent electro-optical performances, high integration, low voltage operation, low power consumption, low cost, long lifetime, and robustness against environment. The monolithic integration of UV, visible and infrared detectors on the same uncooled CMOS smart system would therefore represent a major advance in the combat field, for characterization and representation of targets and backgrounds. In this approach, we have recently developped a novel technology using polymorphous silicon. This new material, fully compatible with above-IC silicon technology, is made of nanometric size ordered domains embedded in an amorphous matrix. The typical quantum efficiency of detectors made of this nano-material reach up to 80 % at 550 nm and 30 % in the UV range, depending of the design and the growth parameters. Furthermore, a record dark current of 20 pA/cm² at −3 V has been reached. In addition, this new generation of sensors is significantly faster and more stable than their amorphous silicon counterparts. In this paper, we will present the relationship between the sensor technology and the overall performances.

To investigate the possibility of battlespace characterization, including the ability to classify munitions type and size, experimental data has been collected remotely from ground-based sensors, processed, and analyzed for several conventional munitions. The spectral, temporal and spatial infrared signatures from bomb detonations were simultaneously recorded using a Bomem MR157 Fourier Transform Infrared Spectrometer and an Indigo Systems Alpha Near-Infrared camera. Three different high explosive materials at three different quantities each were examined in one series of field studies. The FTIR spectra were recorded at 4 cm^-1 spectral resolution and 123-ms temporal resolution using both HgCdTd (500-6000 cm^-1) and InSb (1800-6000 cm^-1) detectors. Novel key features have been identified that will aid in discriminating various types and sizes of flashes. These features include spectral dependent projections of one event's temporal data onto another event's temporal data, time dependence of the fireball size, ratios of specific integrated bands, and spectral dependence of temporal fit constants. Using Fisher discrimination and principal component techniques these features are projected onto a line that maximizes the differences in the classes of flashes and then identify the Bayesian decision boundaries for classification.

Infrared emissions from the detonation of three bomb types and four weights in a series of 56 events were recorded by a Fourier transform interferometer in the mid-IR (1800-6000 cm^-1) at temporal and spectral resolutions of 0.047 s and 16 cm^-1, respectively. Spectrally-resolved time profiles from two representative detonation events were selected for this study. Two parameterized, empirical functions adequately represent the temporal signatures taken from four spectral bands in which atmospheric attenuation losses are small. Each function is the sum of either two or three exponential terms modulated by delayed switching functions. The number of exponential terms required to fit each temporal profile depends on the explosive and varies with the frequency. The dimensionality of the spectrally-resolved temporal signatures is significantly reduced by establishing these characteristic timescales. Such feature extraction is critical for proposed event classification schemes.

The addition of an advanced EO subsystem to an in-service tracker system is reviewed in terms of the sensor modelling and proving activities. For the latter, emphasis is placed on model verification and validation techniques that will lead to a validation case which will then be used to gain equipment acceptance with the UK Royal Navy. The approach to modelling encompasses parametric and image-flow models. The relationship between these different representations is described together with their interaction with the EO equipment and the project development lifecycle. The algorithms generated for the image flow model will be used as the basis for the EO subsystem detection, tracking, and data association software. Issues arising from model validation activities are addressed in detail and include the validation approach, appropriate metrics, coverage of the operational envelope and the use of synthetic imagery to augment trials data.

This paper describes the methodology for executing real-time simulations for the support of field testing of smart munition sensors. The sensor simulated is a dual-mode sensor using passive thermal infrared and active laser profiling. The types of tests supported by the simulation are dynamic flight tests over stationary targets, captive flight tests with moving tactical targets, and end-to-end system tests with dynamic flight over moving tactical targets. The components of this methodology that will be discussed include the sensor simulation model, target and background models, and measurement procedures for generating inputs required for target and background models. The resulting simulation capability can be used to support a wide range of evaluations including concept evaluation, subsystem design trade-off analysis, and system performance evaluation.

Camouflaged objects in a background, which are not possible to observe with conventional IR measurements without polarization, can be seen in polarization measurements. In this paper will be shown that polarization measurements increase the possibility to detect covered objects. The denial of polarization measurements of a covered object has earlier been achieved by construction of a surface covered with cenospheres. The emissivity as a function of angle of incidence has also been investigated on this newly developed surface, which can be designed to have emission properties decided in advance. The results indicate that it is possible to use the surface materiel as a means to adapt an object to a certain optical signature. It has also been shown that the surface almost completely depolarises the emitted radiation, which makes it more difficult to observe with a polarization measurement. These properties make the surface suitable as a reference surface for polarization measurements. A more systematic development of these surfaces and investigation are reported here.

The Advanced Technology Centre (ATC) is responsible for developing IR signature prediction capabilities for its parent body, BAE SYSTEMS. To achieve this, the SIRUS code has been developed and used on a variety of projects for well over a decade. SIRUS is capable of providing accurate IR predictions for air breathing and rocket motor propelled vehicles. SIRUS models various physical components to derive its predictions. A key component is the radiance reflected from the surface of the modeled vehicle. This is modeled by fitting parameters to the measured Bi-Directional Reflectance Function (BDRF) of the surface material(s). The ATC have successfully implemented a parameterization scheme based on the published OPTASM model, and this is described. However, inconsistencies between reflectance measurements and values calculated from the parameterized fit have led to an elliptical parameter enhancement. The implementation of this is also described. Finally, an end-to-end measurement-parameterization capability is described, based on measurements taken with SOC600 instrumentation.

Missile warning systems operating in the ultraviolet part of the spectrum have become a common part of the suite of self-defence systems of modern aircraft. These systems have a low false alarm rate and a detection range of several kilometers against man-portable surface-to-air missiles. The performance of the missile warning systems depends on several factors, including weather and threat type. This paper uses a generaic missile warning sensor and a recently developed model to predict missile plume UV radiance, to demonstrate the variability in detection range for a number of typical threats, weather types, aircraft speeds and warning system lay-outs. The variation in sensor performance present in the results shows that an assessment of the level of platform self-protection prior to each mission should be performed.

The standard method used for modeling optical turbulence effects on imaging uses an optical transfer function (OTF). To model this function the short- and long-exposure limiting cases exist. The short-exposure case is handled by modifying the long-exposure case to remove wavefront tilt assessed at the sensor entrance pupil. Then, depending on whether one is in the "near-field" or the "far-field," one of two subcases is used. These evaluations require a model of the refractive index spectrum. Typically this model is assumed to be the Kolmogorov spectrum where an inner scale is set to zero and outer scale is infinite. However, for real atmospheres the inner and outer scales affect turbulence predictions through a modified spectrum. The difficulty using non-limiting values for these parameters is that double integrals must then be assessed. However, in this paper analytic forms are developed to describe the spectrum, permitting analytic solutions to these integrals. The result is that we can express quantities such as the Fried coherence diameter in closed form accounting for both inner and outer scale effects. Also, expressions for the inner and outer scales of turbulence can be written as functions of the atmospheric surface layer stability. Lastly, it is shown that the near/far-field effect does not easily subdivide into two cases. In fact, the distance dependence of the tilt effect is shown to span a range of 107 in the governing dimensionless parameter. To model this continuum a unified treatment is considered.

Ontar Corporation is a research and development company specializing in the development of products for atmospheric remote sensing. Over the past two decades, Ontar has been the leading supplier of software for meteorological applications in remote sensing, radiative transport and atmospheric propagation. Ontar's complete line of software for modeling of varying weather conditions includes the MS Windows-based PcModWin, PcLnWin, HitranPC, E-Trans, NVTherm, IICCD, IIMRC and SSCAM software, as well as JavaHAWKS, a HITRAN database manipulator. This paper will describe both the updates to the existing products and the new products recently released to the user community. Ontar has been continuing its efforts to enhance its product capabilities in atmospheric modeling. Recent enhancements include the addition of new functionalities and the incorporation of new models into existing software such as PcModWin and PcLnWin. This paper will discuss these new added models and functionalities in detail. It will also demonstrate how these enhancements improve the atmospheric modeling in remote sensing applications.

The process of integrating high-fidelity, complex dynamic scene projection systems into space simulation test chambers is a continual challenge which requires comprehensive analysis and measurement of the properties of the optical components involved. This includes the multiple-band source subsystems and the spectral tailoring methods invoked to represent target temperatures. Techniques currently employed in the AEDC space sensor test facilities will be discussed in this paper.

Recent world events have accelerated the evolution of the US military from monolithic formations arrayed against a known enemy, to a force that must respond to rapidly changing world events. New technologies are part of the Army's evolution and thermal imaging sensors are becoming more and more prevalent on the modern battlefield. These sensors are integrated into advanced weapon systems or commonly used for battlefield surveillance. Thermal imaging systems give the soldier the ability to deliver deadly force onto an enemy at long ranges at any time of day or night. The ability to differentiate friendly and threat forces in this situation is critical for the avoidance of friendly fire incidents and for the proper use of battlefield resources. The ability to foresee the location of the Army's next battlefield is becoming more difficult, and we don't know where the next battlefield will be from year to year. Infrared target recognition training tools need to be flexible, adaptable, and be based on not only the latest intelligence data but have geographically specific training available to the soldier. To address this training issue, personnel of the Measurement and Signatures Division at the National Ground Intelligence Center have created the Simulated Infrared Earth Environment Lab (SIREEL) web site. The SIREEL web site contains extensive infrared signature data on numerous threat and friendly vehicles and the site is designed to provide country-specific vehicle identification training in support of US military deployments. The bulk of the content currently on the site consists of infrared signature data collected over a decade of intelligence gathering. The site also employs state of the art infrared signature modeling capabilities to provide the soldier in training the most flexible training possible. If measured data on a vehicle is not available, the website developers have the capability to calculate the infrared signature of ground vehicles in any location, any type of terrain, any weather condition, any operational state, at any time of day on any day of the year. This allows the SIREEL website developers to completely populate target signature training databases when measured data is unavailable for required vehicles. This paper explores the methodologies and tools necessary to provide the predictive infrared ground vehicle signatures for this application.

The Air Force Research Laboratory, Air Vehicles Directorate, Control Sciences Division, AFRL/VAC, has developed a process for constructing large-area 3-D multispectral terrain databases to support Simulation Based R&D (SBR&D) concept development simulation, research and T&E activities. The Multispectral database (MsDB) development effort was intended to investigate a database development approach built from multispectral/hyperspectral imagery and elevation data that also incorporates material classification data capable of supporting realistic out-the-window visual and multispectral terrain displays, weapons/sensor models, and cockpit representations immerse the warfighter, scientist, analyst, and testers into a dynamic environment suitable for engineering level test and evaluation of a multitude of systems. This paper also covers integration of the MsDB with Paint the Night IR Scene Generation; integration with Mutli-Mode Radar Simulation (MMRS), which provides Synthetic Aperture Radar Simulation; and integration with SubrScene, which provides Out-The-Window Scene generation.

Methods are examined for modeling signature propagation through optically dense atmospheres accounting for both direct signal attenuation, isotropic scattering, and strong forward (multiple) scattering considering both existing analytical approaches and numerical Monte Carlo photon transport simulations. Examples are given for the case of upward propagation through plane layers in the form of angular distribution functions, based on a point source of light, from which one can determine an atmospheric modulation transfer function (MTF) widely used in simulating the effects of obscurants in imaging systems performance models. Methods can also be used to estimate correction factors for compensating errors in transmission measurements due to multiple forward scattering into the sensor field of view. Results suggest that even at large optical thicknesses where the directly transmitted signal is minimal, there is enough spatial contrast in the diffuse signal to make point sources detectable. However the major effect is on the broadening of the received signal which for the case of isotropic scattering yielded values on the order of 5-10 m for the width of the point spread function (PSF) for a sensor field of view of 100 milliradians. For more intense forward scattering the relative signal strength is increased and the PSF widths are decreased.

Methods are examined for modeling ultra narrow band signature propagation through both natural and optically dense or obscured atmospheres. The impetus for the study comes from the recent exploitation of (ultra)narrow band atomic line filters which have become practical for remote sensing applications and real battlefield sensors. Aside from the obvious advantage of being able to "squeeze" the narrow-band signal through (near) equally narrow-band "windows" of the natural (gaseous) atmoshpere; there are a number of other issues that come into play that are either negligible or irrelevant in the more usual broadband applications but may be important here. For example, another way the technology can be exploited is by selecting the filter wavelength so as to take advantage of the Fraunhofer absorption lines in the solar spectrum, thsu producing an effective "solar blind" sensor and the attendant advantages thereof. In this paper we address both practical issues such as line broadening by various known atmosphere processes, including extinction and scattering by suspended aerosols and adverse weather, as well as some more subtle issues such as the effect of the wavelength shift due to atmospheric refraction and Doppler shifting due to the relative motion of the Earth with respect to the Sun; both of which though admitted small and thus negligible in broadband applications could be important here. Our main technical approach is through simulation using conventional models such as MODTRAN and EOSAEL (battlefield atmospheres), augmented as necessary for the task at hand. Preliminary results based on side-by-side comparisons with conventional broadband technologies (e.g., interference filters, Δλ≈10nm, FWHM) are discussed and shows both advantages and disadvantages of the narrow-band technology.

The identity of the minimum resolvable temperature difference MRTD and the minimum detectable temperature difference MDTD for the thermal imaging systems (TIS) will be proven and new unite expression fo MRTD and MDTD will be derived in this paper, which is important for researching new performance model of thermal imaging systems.

The time-dependent Mueller matrix solution of vector radiative transfer for inhomogeneous random media of non-spherical scatterers is presented. Co-polarized and cross-polarized bistatic scattering for a polarized pulse incidence are numerically simulated. Numerical results well demonstrate volumetric and surface scattering mechanism and depict the inhomogeneous fraction profile of random scatterers. Effective canopy height inverted from the pulse echo is discussed. Numerical simulation shows detection of anomalous scatters under inhomogeneous canopy.

An international multisensor measurement campaign called "MUSTAFA" yielded many infrared image sequences of differently camouflaged targets. The image sequences were acquired by a helicopter sensor platform approaching the targets. The effectiveness of the various camouflage methods still has to be evaluated. Apart from observer experiments, FGAN/FOM and IITB pursue an ATR (Automatic Target Recognition) -based method for the automatic evaluation of the camouflage variants. The ATR approach consists basically of the detection component of an ATR for reconnaissance purposes in forward-looking infrared image sequences (FLIR). Given some flight and sensor parameters the algorithm can report detection hypothesizes together with a measure of confidence and the detection range for each hypothesis. Proceeding on the assumption that better camouflage yields late automatic detection of the corresponding target in approaching image sequences, the detection range output of the algorithm could be an additional criteria for camouflage evaluation. The paper presents some aspects of the reconnaissance detection algorithm, detection ranges for exemplary image sequences of the MUSTAFA data set, and future options: e.g. real-time operation in the sensor platform during the measurement campaign.

Fire detection has been an active research field for many years and a number of algorithms have been proposed. These algorithms, however, are often inflexible in dealing with the spatial and temporal heterogeneity of the environment. Different biomes, seasons, and temperatures usually cause the performance of these algorithms to vary dramatically. In this paper, we propose a new algorithm for fire detection based on the Mahalanobis distance that exploits the statistical properties of multi-spectral images. The distinguishing feature of our algorithm is its robustness. It can effectively differentiate fire from background in various environments, using a single, fixed threshold. We evaluate our algorithm by comparing it to three state-of-the-art existing algorithms: the MODVOLC normalized fire index algorithm, the Arino's threshold algorithm, and the contextual MODIS algorithm. All algorithms are tested using MODIS images taken in different parts of the world as well as at different times. Experimental results demonstrate that our algorithm consistently achieves the best performance, showing a low and constant false alarm rate.