Infrared images in the 3 to 5 and 8 to 12 micron band were taken of soldiers wearing various camouflaged uniforms. The soldiers wearing the uniforms were either standing, crouched or prone. The images were presented to 37 observers and their detection decisions analyzed. Results were analyzed to determine which uniforms offered the most protection to a threat sensor at various ranges. The perception laboratory results were modeled using the Fuzzy Logic Approach and the CAMAELEON model with a resulting Pearson correlation of 0.9.

To better characterize the background clutter seen by infrared warning receivers (IRWR), a methodology was developed which divided the background environment into six categories: (1) rural, (2) urban below 6,000 feet above- ground-level (AGL), (3) urban above 6,000 feet AGL, (4) industrial, (5) battlefield, and (6) extended fire. Each of these environments has unique characteristics which separate it from the other environments and these characteristics can be used by the system designer to improve the system false alarm rate. A flight test was conducted in these environments to collect IRWR response data and attempt to identify IR sources which may cause false alarms. This paper describes the different environments, techniques developed to identify false alarm sources and lessons learned from the flight test.

Signature prediction models have become an increasingly important tool for the ground combat vehicle designer in recent years. System designers have been successful in prototyping entire vehicles in each spectral band. With this success, focused efforts to improve the accuracy of these signature models have produced robust, validated performance for many operational conditions. One of the most recent improvement in prediction models for ground vehicle systems has been improvements in surface reflectance. Surface reflectance is central to the predicted performance of these models and range from simple to very complex. Simple surface reflectance models treats the surface as totally lambertiant has an advantage of being fast to calculate but does not take into account the specular nature which all surfaces posses. The bi-directional reflectance distribution function (BRDF) is a more complex representation which allows for a more accurate representation of surface reflectance phenomena. The input to the BRDF usually comes from a laboratory sample measured in a laboratory setting. These laboratory samples are made to be perfect so that comparisons can be made between variations in formulas for the coatings. The limitation of these inputs is that surfaces that are exposed to environments effects and normal daily use are the more representative of data we are interested in. Other effects such as the conditions under which the surface coatings are applied can cause reflectance variability as well. This paper explores the variability on real targets and compares them to laboratory samples. The implication of these variations to signature models will be explored.

The ability to detect targets, either by human observer or by machine vision algorithms, is strongly affected by the clutter properties of the background. Therefore, the quantitative characterization of the clutter is a must in any target acquisition scenario. This paper will propose a new metric for clutter quantification based on blob analysis algorithm. The algorithm consists of comparison of various calculated properties of the blobs, found in the image, to the properties of the blob representing a target. The algorithm was tested on an experimental database of IR images measured during an air-born field test by a commercially available Focal Plane Array IR camera.

Detectability of man made objects in natural environment is strongly affected by the clutter properties of the background. To improve the ability to detect and recognize targets by human observers or by machine vision (ATR algorithm) additional information obtained from polarization properties can be exploited. This paper presents an experimental set up for the measurements of the polarization signature of man made objects based on a commercial FPA IR camera and an externally mounted linear polarizer. Some typical results obtained by this system are presented.

Automatic target detection (ATR) generally refers to the localization of potential targets by computer processing of data from a variety of sensors. Automatic detection is applicable for data reduction purposes in the reconnaissance domain and is therefore aimed at reducing the workload on human operators. ATR covers activities such as the localization of individual objects in large areas or volumes for assessing the battlefield simulation. An increase of reliability and efficiency of the overall reconnaissance process is expected. The results of automatic image evaluation are offered to the image analyst as hypotheses. In this paper cluttered images from an infrared sensor are analyzed with the aim of finding Regions of Interest (ROIs), where hints for man-made objects have to be found. This analysis uses collateral data from acquisition time and location (e.g. day time, weather condition, resolution, sensor specification and orientation etc.). The assumed target size in the image is also compared by using collateral data. Based on the collateral data, the algorithm adjusts its parameters in order to find ROIs and to detect targets. Low contrast conditions can be successfully tackled if the directions of the grey value gradient are considered, which are nearly independent of the contrast. Blobs are generated by applying adaptive thresholds in the ROIs. Here the evaluation of histograms is very important for the extraction of structured features. The height, aspect angle, and camera parameters are approximately known for an estimation of target sizes in the image domain out of the collateral data.

Shape and shape disruption have significant influence to the human target acquisition mechanism. A special testing method (the so called `photo-simulation') was developed in the eighties to present a set of image slides of camouflaged and not camouflaged objects in preferably natural backgrounds to military personnel to quantify differences in object camouflage effectiveness. Statistically significant results were achieved, however, the high test requirements limited its practical use. The project is motivated by an urgent need for a camouflage evaluation system based on computer vision with a fast response so that the user in a field test can be supported to further improve his camouflage skills. Hence, the photo simulation method cannot be regarded as obsolete, it can be used to compare the results of the camouflage evaluation system with the results of human perception. With an human-in-the-loop computer based camouflage assessment system, processing should be sped up by some orders of magnitude, could be automated for field tests and would yield several additional features. To overcome the problem of quantifying e.g. texture similarity of different camouflage nets to blend into the natural background, an image processing/visualization method was pursued by the Austrian Ministry of Defense. Now the same image-sets can be used for the human photo-simulation as well as for segmentation/classification by the camouflage assessment tool. Today a modified Euclid-distance measurement for visual images is being used while similarity of shapes (gestalt) to a selected region can be visualized. Feature selection is being done by training a neural network with the results of the human perception data. A cost effective prototype of a camouflage assessment tool based on standard hardware can be presented. Its promising performance gives hope to get beyond subjective camouflage experts stimuli. In the next project phase also thermal images shall be handled with the camouflage assessment tool.

The Irma synthetic signature model was one of the first high resolution infrared (IR) target and background signature models to be developed for tactical weapons application. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory, the Irma model was used exclusively to generate IR scenes for smart weapons research and development. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser channel. This two channel version, Irma 3.0, 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. This and other improvements were released in Irma 2.2. Irma 3.2, 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. Currently, upgrades are underway to include a near IR (NIR)/visible channel; a facet editor; utilities to support image viewing and scaling; and additional target/data files. The Irma 4.1 software development effort is nearly completion. The purpose of this paper is to illustrate the results of the development. Planned upgrades for Irma 5.0 will be provided as well. Irma is being developed to facilitate multi-sensor research and development. It is currently being used to support a number of civilian and military applications. The current Irma user base includes over 100 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry.

To understand how a human operator performs visual search in complex scenes, it is necessary to take into account top- down cognitive biases in addition to bottom-up visual saliency effects. We constructed a model to elucidate the relationship between saliency and cognitive effects in the domain of visual search for distant targets in photo- realistic images of cluttered scenes. In this domain, detecting targets is difficult and requires high visual acuity. Sufficient acuity is only available near the fixation point, i.e. in the fovea. Hence, the choice of fixation points is the most important determinant of whether targets get detected. We developed a model that predicts the 2D distribution of fixation probabilities directly from an image. Fixation probabilities were computed as a function of local contrast (saliency effect) and proximity to the horizon (cognitive effect: distant targets are more likely to be found c close to the horizon). For validation, the model's predictions were compared to ensemble statistics of subjects' actual fixation locations, collected with an eye- tracker. The model's predictions correlated well with the observed data. Disabling the horizon-proximity functionality of the model significantly degraded prediction accuracy, demonstrating that cognitive effects must be accounted for when modeling visual search.

The EOSAEL is a state-of-the-art computer library comprised of fast-running, theoretical, semiempirical, and empirical computer programs that mathematically describe aspects of electromagnetic propagation in a battlefield environment. The 25 modules are connected through an executive routine, but often are exercised individually. The Army Research Laboratory (ARL) is in the process of developing new modules for inclusion into the EOSAEL. This presentation discusses these modules and describes ARL's plans for future additions, and planned measurement programs. A program is underway to develop a user-friendly, Microsoft Windows-based interface for the EOSAEL. This interface will allow the user to set parameters by an intuitive, visual point and click method, output plots of computed results, and generate images that are easily viewed. The interface will be independent of the internal workings of the individual EOSAEL modules, and have on-line documentation for all the modules and input parameters. It will simplify setting scenario parameters common to all the modules (e.g. spectral band, range, etc.), and will provide tools to easily compare the results from the different EOSAEL modules. Interface modules are completed for all of the EOSAEL modules. This effort is part of a commercialization plan sponsored by the SBIR program.

Nineteen ninety-eight marks the 25th anniversary of the release of the first HITRAN database. HITRAN is recognized as the international standard of the fundamental spectroscopic parameters for diverse atmospheric and laboratory transmission and radiance calculations. There have been periodic editions of HITRAN over the past decades as the database has been expanded and improved with respect to the molecular species and spectral range covered, the number of parameters included, and the accuracy of this information. The 1996 edition not only includes the customary line-by-line transition parameters familiar to HITRAN users, but also cross-section data, aerosol indices of refraction, software to filter and manipulate the data, and documentation. This paper describes the data and features that have been added or replaced since the previous edition of HITRAN. We also cite instances of critical data that is forthcoming. A new release is planned for 1998.

This paper describes a novel method for enhancing the infrared signature of surrogate or decoy armored vehicles. The method consists of using water tanks of a calculated thickness to emulate the heat capacity of thick metal on armored ground vehicles. Extensive experiments have shown that the surface temperature of the water tanks closely matches the surface temperature of thick metal plates over an entire diurnal cycle. Passive infrared signatures are duplicated since the surrogate vehicle component has the same innate thermal characteristics of the vehicle component to be emulated. Multiple fidelity, cost, and labor saving advantages are included by using water tanks. Infrared signature fidelity is enhanced since the water tank will have the correct surface temperature irregardless of location, time of day, time of year, or weather conditions. No external heat inputs are necessary to constantly adjust the water tank's temperature. The tanks can be made from any thickness of metal which greatly reduces the cost of materials and labor to construct a surrogate or decoy vehicle. This paper presents the derivation of the design equations for the water tanks as well as extensive experimental results under a variety of environmental conditions. In addition, results are presented for a similar concept where the equivalent heat capacity concept is used to replace solid aluminum with thinner plates of solid steel.

This paper presents a top-level description of methods used to generate high-resolution 3D IR digital terrain databases using soft photogrammetry. The 3D IR database is derived from aerial photography and is made up of digital ground plane elevation map, vegetation height elevation map, material classification map, object data (tanks, buildings, etc.), and temperature radiance map. Steps required to generate some of these elements are outlined. The use of metric photogrammetry is discussed in the context of elevation map development; and methods employed to generate the material classification maps are given. The developed databases are used by the US Army Aviation and Missile Command to evaluate the performance of various missile systems. A discussion is also presented on database certification which consists of validation, verification, and accreditation procedures followed to certify that the developed databases give a true representation of the area of interest, and are fully compatible with the targeted digital simulators.

A simple and efficient psychophysical procedure is presented to quantify the visual conspicuity of a target in a complex (natural) scene. Measurements can easily and quickly be performed in the field or in complex environments. Only a few observers (typically 2-3) are need to achieve sufficient accuracy. The present study shows that this conspicuity measure predicts human visual search performance in realistic and military relevant complex scenario's. Also, conspicuity measured on photographic slides agrees with conspicuity measured in the field. This implies that the new conspicuity measure can be used in combination with photosimulation studies to optimize and evaluate the visual distinctness of displayed information.

Detecting and characterizing motion in a scene can play a critical role in target detection algorithms, since many targets can be camouflaged so completely that, if they are not moving, they are nearly undetectable. However, once they begin moving, they `popout' and are immediately detected. Estimating motion is also important in human vision modeling, because motion is generally detected with peripheral vision, which can cover the field of regard much more quickly than foveal vision. In this paper, we present two hierarchical multiresolution methods for computing the optical flow in a scene. We use statistical properties of the resulting flow fields to compute a motion feature vector, which we relate to the conspicuity of the moving target in a scene via a neural network.

This paper summarizes N.P. Travnikova's model as a method to compare average search times by military observers using powered optics such as binoculars. Both discrete and continuous scanning methods are considered for target searches. This empirical model quantifies which type of vision system is best suited for the most efficient target detection for a given field of view. An analysis is also provided of the relative importance of target diameter, background luminance, and contrast upon overall detectability with the subsequent results compared to known field test data. The detectability of specific military ground vehicles over a variety of search and target acquisition tasks with several off-the-shelf binoculars is examined. Some examples on various types of search studies, such as compare looking at the target, both line retrace time effects, etc., for a low contrast target are also considered. This paper consists of two sections. The first explains the derivation of methodology and limits of its applicability. The second section offers a parametric analysis that compares the relative importance of target diameter, background luminance, and contrast upon overall target detectability with the subsequent results compared to field test data.

An analysis results of Vis&NIR LIDAR limitations and improving possibilities show that specifications for electro-optical instrument parameters should be formed on the basis of stability-against-background clutter analysis of receiving signals' processing algorithms used. It allows to formulate the requirements to a measurements accuracy that often can be determined by a background clutter influence.

Clutter can be a significant challenge to the detection, tracking and discrimination abilities of infrared seekers and sensors, creating considerable interest in techniques for its characterization and synthesis. In the past, clutter power spectral density models have been used both to analyze and generate infrared clutter images. Many of these models assume stationary statistics for the clutter process (i.e., the clutter is homogeneous). For many real images, however, nonstationary statistics are needed for characterization. Likewise, more realistic synthetic images can be generated through the use of nonstationary statistics. This paper presents a technique for segmentation of an image into homogeneous regions and for subsequent synthesis of a similar image from white noise. For the segmentation, two features, the slope of the local-area power spectrum and the local-area mean, were first used to characterize each pixel in the image. Then, using the feature values, a 2D histogram was formed and a Bayesian decision process was used to cluster pixels with similar features into a small set of classes. The synthesis technique uses an adaptive spatial- domain filter to generate clutter images from white noise.

The Signature Technology Laboratory of the Georgia Tech Research Institute has nearly 10 years experience in the analysis, modeling and simulation of imaging infrared missile seekers. This experience has led to the development of an integrated Imaging Simulation for Infrared Sensors that has been applied to a range of problems from imaging seeker signal processing development to imaging infrared countermeasure concepts exploration. This paper will describe the development of a closed loop model which has the missile seeker signal processor drive the missile gimbal platform line-of-sight, which in turn is used to provide guidance signals to the missile autopilot. The infrared scene generation is briefly described, with emphasis placed on the sensor and signal processor subsystems. Results of test cases are shown, and applications are discussed.

The goal is to achieve a model of radar sea reflection with improved fidelity that is amenable to practical implementation. The geometry of reflection from a wavy surface is formulated. The sea surface is divided into two components: the smooth `chop' consisting of the longer wavelengths, and the `roughness' of the short wavelengths. Ordinary geometric reflection from the chop surface is broadened by the roughness. This same representation serves both for forward scatter and backscatter (sea clutter). The `Road-to-Happiness' approximation, in which the mean sea surface is assumed cylindrical, simplifies the reflection geometry for low-elevation targets. The effect of surface roughness is assumed to make the sea reflection coefficient depending on the `Deviation Angle' between the specular and the scattering directions. The `specular' direction is that into which energy would be reflected by a perfectly smooth facet. Assuming that the ocean waves are linear and random allows use of Gaussian statistics, greatly simplifying the formulation by allowing representation of the sea chop by three parameters. An approximation of `low waves' and retention of the sea-chop slope components only through second order provides further simplification. The simplifying assumptions make it possible to take the predicted 2D ocean wave spectrum into account in the calculation of sea-surface radar reflectivity, to provide algorithms for support of an operational system for dealing with target tracking in the presence of multipath. The product will be of use in simulated studies to evaluate different trade-offs in alternative tracking schemes, and will form the basis of a tactical system for ship defense against low flyers.

The scope of the paper deals with the prediction of the RCS of nonmetallic structures. The scattering matrix of a complex object is derived from the Physical Optics Method and the Method of Equivalent Currents. Both tools need the material parameters or the Fresnel reflection factors as input values. In a realistic world scene the material parameters or the reflection factors in general can be evaluated by measurements only. For this purpose two different principles are applied, namely the measurement of S-parameters in a waveguide followed by the determination of the material parameters and the measurement of reflection factors under free space conditions thus allowing cross checks of the individual results which cannot be free of errors. To demonstrate the influence of measurement errors a square metallic panel was used which was covered with two different ferromagnetic layers for which different sets of material parameters exist due to different measurement principles and investigations at independent laboratories The resulting Fresnel reflection factors, Brewster angles and radar cross sections are presented and discussed.

This paper considers the operation of multi-element radar arrays in the context of Airborne Early Warning applications. A 2D convolution model is proposed to represent the transformation of data determined by the existence of targets characterized by a given relative velocity and located at a certain angle, into a corresponding Azimuth-Doppler Spectrum. The feasibility of this interpretation is demonstrated by matching of the spectrum generated through convolution with the one resulting from software simulation of the same target conditions. Two methods of discrete 2D deconvolution are explored in an attempt to revert the process, obtaining an estimate of the target characteristics from simulated Azimuth-Doppler Spectra. The advantages and disadvantages of the methods are reported and the possibility of using deconvolution to preferentially retrieve target components over clutter interference is presented.

The need for through-the-wall surveillance sensors has existed for many years. Recent advances in microwave and millimeter-wave (MMW) technologies provide new applications for law enforcement use. These applications include the potential to conduct surveillance through walls and the ability to detect the presence of living persons behind doors or other barriers. Covert surveillance and personnel detection are of high interest to both the Department of Defense in support of Small Unit Operations and the Justice Department for civilian law enforcement applications. Microwave sensors are under development that can detect the presence of persons (and even weapons) behind walls and track moving persons behind walls. MMW sensors are under development which can provide pseudo-images of persons behind the walls including radiometric sensors at 95 GHz, active 95 GHz real aperture radars, and heartbeat detection radars. Radiometric sensors include 2D FPA systems, 1D FPA, scanned systems, and single element scanned sensors. Active FPA radars include illuminated radiometric systems and coherent radar systems. Real aperture MMW radar systems include raster scanned and non-scanned (hand-held) sensors.

Factors that affect the efficiency of a phase-correcting Fresnel zone plate (FZP) lens antenna are analyzed. These include attenuation, diffraction, and reflection losses. The FZP efficiency is compared to that for a standard lens, and the FZP is shown to be better at millimeter wavelengths, where attenuation is an important factor.

This paper describes the investigation and potential utility of using the Direct Write Scene Generator (DWSG) to project onto a sensor system's focal plane array (FPA) through its optical telescope. The test approach requires development of magnifier/collimator systems to expand the DWSG output to the sensor telescope. A lens system has been procured to facilitate the projection to the full array of a standard CCD with an attached lens system. A demonstration of operation of the DWSG through camera optics has been recorded. The capability of the DWSG system to measure FPA crosstalk has been examined in some detail.

Using synthetic background scenes in the modeling of thermal infrared sensor-based smart munitions offers tremendous flexibility in exploring the performance envelope of these systems. However, to reach this goal, the synthetic background generation process must undergo the scrutiny of verification and validation to be accredited for use with a specific sensor system. Traditional approaches to validating synthetic scenes range from low-level subjective comparison to absolute pixel-to-pixel agreement between the two scenes. Neither of these approaches considers the specific smart munition sensor and processor which ultimately use the scene. In this paper we present an alternate validation approach based on comparison between end performance of a thermal infrared sensor-based smart munition system using synthetic/real scene pairs. Paired synthetic/real thermal scenes, including a low and a high-clutter level, are compared with conventional validation metrics and with the performance-based metric, using various smart munition sensor targeting algorithms. The degree of scene fidelity (absolute agreement between scene pairs) required to replicate performance varies with clutter level and processor algorithm. Under high clutter conditions, greater synthetic scene fidelity is required to match performance.

The increased availability and reliability of hyperspectral thermal imaging systems has stimulated the development of new exploitation algorithms that key on spectroscopic features. However, variations in image formation parameters can have significant effects on the ability of hyperspectral algorithms to separate and identify spectral signatures within a scene. To continue to support the algorithm development community, the simulation community must provide synthetic imagery that features both the spatial and spectral characteristics observed in actual imagery. This paper outlines the spectral resolution requirements for hyperspectral simulation in the thermal region as well as the importance of rigorous modeling of the atmosphere, surface temperatures, surface optical properties and backgrounds contributions. The approaches taken by the Digital Imaging and Remote Sensing Image Generation model to incorporate these critical modeling parameters are described and example imagery demonstrating variations of these parameters is also included.

Physics Innovations Inc. has developed polarization- sensitive imaging sensors with high-spatial resolution. In this paper we describe methods where video frames are captured and processed into images of the target's temperature distribution and the target's 3D shape and orientation. We demonstrate that the two angles describing surface orientation can be determined for every point on a target. For all targets in the field of view, their geometric shapes and temperatures can be determined simultaneously. With high-speed signal processing, high- resolution polarimetric data can be captured and displayed in real time and at video frequency.

This paper presents a suite of techniques called the Adaptive Wavelet-based Contrast Enhancement Method (AWCEM) for improving the subjective quality of an image for observation by a human. The central idea in these techniques is the space-varying stretching of the contrast of an image by enhancing or attenuating its detail wavelet transform coefficients. The degree of stretch is governed by a multi- resolution region of interest mask which is generated by a low-level feature extraction mechanism. Intelligent image enhancement seeks to preserve and amplify target details while at the same time suppressing the background clutter. This calls for an enhancement mechanism that (1) is adaptive across the image, (2) performs some form of low-level feature extraction and (3) uses these features to control the level of contrast stretching. The proposed algorithms use a combination of the multiscale energies, edge strengths, texture and motion as the features. Local scale- space anomalies in the image seed the regions of interest. We present the suite of techniques within an interactive environment, the AWCEM Tool, which allows the user to choose and optimize the algorithm for the particular type of data encountered in the final application. The algorithm applies well to both single-channel IR and visual imagery.

The Kestrel Corporation visible-near IR band (525 to 1016 nm) airborne Fourier Transform Hyperspectral Imager was modified to include measurement of the polarization characteristics of several ground cover classes. The polarization contrast of typical terrestrial background and target objects was characterized. First, the t statistic was used as an index of class separation to determine whether polarized images were more useful for discriminating several cover classes than unpolarized images. Second, the information present in polarized images which is not present in unpolarized images was identified and described. This was done by regressing polarized and unpolarized images, generating images of predicted values for the polarized images using the regression coefficients, generating images of residuals by subtracting the actual values from the predicted values, and analyzing the statistical separation of cover classes in the residual images. A single polarized image was not more useful for identifying the cover classes than an unpolarized image. A residual image derived from a single polarized image and an unpolarized image provided a mean maximum statistical separation of t equals 18.3 for all cover class combinations. The sum of two orthogonal polarized images provided slightly greater separation, with a mean maximum separation of t equals 23.7.

Data and analyses demonstrating the variability and uncertainties in infrared (IR) ground target signatures are presented. The uncertainties are due to a variety of factors ranging from environmental effects to differences in vehicle configurations. Caution must be exercised when using predictive models for simulations because these models are usually pristine and present repeatable signatures for a given set of inputs. Actual vehicle signatures show the effects of wear and tear, aging, poor maintenance, etc., and these effects will vary from vehicle to vehicle. Vehicles encountered in real-life often have a variety of crew- specific signature components that will affect the signature of a vehicle as well, such as stowage of supplies or spare parts. It is important therefore to develop the concept of a `representative' target and take into consideration the expected variations from a baseline signature. It is common in an infrared scene simulation to have a single signature for a given type of vehicle when more than one of the vehicles is present in the scene at the same time. Limiting the target data in this manner can lead to biased results as observers and algorithms can memorize a particular signature. To avoid this, ground target signatures used for training simulations and algorithm development should incorporate variability in the target signatures. Varying ground target signatures in this manner will provide for more realistic sensor performance assessment, training, and algorithm development. The primary signature factor affecting training and algorithm development will be vehicle configuration. Model developers often use temperature deltas when assessing the fidelity of an IR signature model. When validating an infrared signature model, whether it is digital or a target surrogate, the model developer should take into account the uncertainty in the target signature caused by measurement errors and target surface optical variations. Portions of a vehicle where paint has been removed have greatly reduced emissivity and often the reflected radiance will be from the sky. This can lead to temperature errors of tens of degrees Celsius. As paint ages or get dirty its optical characteristics change which can also cause infrared signature variations. All of these surface factors (and more) lead to a general uncertainty in the IR signature of a ground vehicle.

At the moment the problem of express atmosphere and objects on the Earth's surface state control in remote probe measurements is very actual. For these purposes the dialog numerical modeling method of complex systems of images registration and formation in remote probe in wide wavelength range was developed. It gives possibility to compensate distortions in optical wavelength in condition of signal/noise ratio equal to 10/1.

The possibilities of retrieving the aerosol particle size distribution (APSD) from measurements of the horizontal infrared transmittance in the lower part of the marine atmospheric boundary layer (LP MABL) are studied. The results of the measurements in Denmark in 1996 and in Scotland in 1949 are considered. The analysis of the spectral behavior of the aerosol attenuation obtained from the Denmark Experiment revealed certain peculiarities inconsistent with today's knowledge about natural aerosol in LP MABL. Although the spectral ranges used in the Denmark Experiment are quite sufficient for performing a successful inversion, it proved to be impossible to invert the aerosol attenuation into APSD by the usual methods, because none of the existing models of natural aerosol conforms to the spectral behavior like the one seen in the Denmark Experiment. Possible causes of the peculiarities of the Denmark data are discussed, and some recommendations for future experiments are suggested. The analysis of the Scotland Experiment revealed a more real spectral behavior of atmospheric aerosol for a significant part of the measurement range. This made it possible to invert the experimental data into APSD and to retrieve the aerosol attenuation at the spectral range where the measurement data were absent. The error of such a retrieval is within the error limits of the initial measurement data.

During the intercept flying of endo-atmospheric hypersonic vehicles, an Infra-Red (IR) sensor may be used to detect, acquire, and track the target. This sensor views its target through an IR window on side of vehicle' head cone. The aero-thermal will make window high temperature. To provide adequate performance of the sensor, the window must be cooled to maintain temperature low and to reduce the sensor's background noise and refraction errors. This report includes four parts. The first part introduces the outline of calculating the temperature distribution of the window and background radiation surround the window; the second part describes `equivalent cone' method to calculate the aero-thermal flow in IR window; the third part introduces the engineering model to calculate the efficient of cooling window, including the temperature distribution of window cooled, etc.; the fourth part describes the window background radiation and the S/N calculating model of IR sensor. Main aforesaid engineering methods and models have been verified by wind tunnel test. The calculating results are very important to design IR cooled window and sensor in supersonic vehicle.

Prior to the 1996 Olympics held in Atlanta, Georgia, several versions of a radar vital signs monitor (RVSM) were developed by Georgia Tech Research Institute researchers. The most recent version RVSM was developed to measure the heart rate of Olympic rifle and bow and arrow competitors to determine if their training allowed them to the detect their heartbeats and if so, whether they were capable of using that training to avoid an approximate 5 milliradian movement of the bow or rifle that occurs each time the heartbeats. The RVSM that was developed was tested to detect the shooter's heartbeat at a distance of 10 meters without the requirement of a physical connection to the subject. It was found that a second channel could be added to the RVSM to detect the shooter's respiration rate from a distance of 20 meters without physical connection between the RVSM and the shooter. The RADAR Flashlight, a spin-off of these predecessor systems developed at GTRI, is the topic of this paper. The RADAR Flashlight was designed to detect the respiration of a human subject behind a wall, door or an enclosed space with non-conductive walls. The use of the system as a foliage penetration radar has also been explored. it has been determined that the RADAR Flashlight is capable of detecting a human hiding within a tree line behind light foliage. This paper describes the current status of the RADAR Flashlight and presents typical test data produced when the system is operated in the laboratory environment.

Using synthetic aperture radars with appropriate signal processing algorithms is a recognized technique for remote sensing applications. A wide spectrum of radar frequencies is used and a high degree of sophistication implies polarimetric and further multichannel approaches. Each frequency band used, exhibits special sensitivities to features of the earth's surface or man-made targets. This is mostly due to the coupling of the electromagnetic waves to backscattering geometries which are related to the radarwavelength. A part of the spectrum which has been covered not very intensely is the millimeterwave region. This may be mostly due to the relatively high atmospheric absorption at millimeterwaves which obstructs the use of such sensors for long range applications. On the other hand for military applications IR-imaging sensors are widely used which suffer even more from adverse transmission properties of the atmosphere. Application of multichannel techniques as polarimetry, multifrequency techniques and interferometry are also done with more ease due to compactness of the hardware and simplicity of processing. As there exist no data which would allow to investigate the potential of multifrequency polarimetric and interferometric mmW-SAR the Millimeterwave Experimental Multifrequency Polarimetric High Resolution Interferometric Imaging System was installed into an aircraft C-160 `Transall' to gather respective data over different land scenarios. The off-line evaluation of the radar data starts with off-line track, calibration and reformatting procedures. Afterwards synthetic aperture processing is applied to these data to generate radar images for co- and cross-polarization at 35 GHz and 94 GHz. As already mentioned above, SAR-processing at millimeterwavelengths requires a considerable lower amount of sophistication in comparison with algorithms applied at lower radar-frequencies. This can mainly be attributed to the short aperture length at mm-wave frequencies. Taking this into account, the SAR-algorithm used here is relatively simple although fully automatic autofocussing is applied, using only radar-data without supply of external INS information. The interferometric evaluation uses phase unwrapping techniques tailored to the high resolution achieved at mm-waves. The paper describes experiments with the interferometric 35/94-GHz-SAR, describes the IFSAR and phase unwrapping algorithms as well as polarimetric segmentation approaches and shows respective results.