With increasingly available high frequency radar components, the practicality of imaging radar for mobile robotic
applications is now practical. Navigation, ODOA, situational awareness and safety applications can be supported in
small light weight packaging. Radar has the additional advantage of being able sense through aerosols, smoke and dust
that can be difficult for many optical systems. The ability to directly measure the range rate of an object is also an
advantage in radar applications. This paper will explore the applicability of high frequency imaging radar for mobile
robotics and examine a W-band 360 degree imaging radar prototype. Indoor and outdoor performance data will be
analyzed and evaluated for applicability to navigation and situational awareness.
Increasingly, the signature management community is demanding modeling tools for a variety of purposes from real-time simulations to complex modeling tasks. RenderView is one of the tools which has been developed and continues to evolve in response to this demand.
The focus of RenderView development has been physics based modeling of high complexity both geometrically and with respect to surface optical properties. RenderView incorporates full bi-directional reflectance distribution function (BRDF) models and measured and calibrated global illumination maps. With these tools comes the capability to evaluate with a very high level of fidelity the impact of vehicle geometric and surface properties on its visible and thermal signature.
A description of RenderView will be presented in terms of its focus on high fidelity models of vehicles and materials. A number of examples will be presented that show how the fidelity of the BRDF impacts the signature via the rendering model.
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.
Two characteristics are critical in the understanding of target signatures, physical surface temperature and surface reflectance. An objects surface reflectance can be thought of as having two major components, the diffuse and specular components. The best way to understand these components is by examining the Bi-directional Reflectance Distribution Function (BRDF). The BRDF provides an understanding of the reflectance behavior of a surface from every incident angle and reflectance angle. With the BRDF one can provide an accurate computer model of how the material behaves. Databases of BRDF data are available for use in modeling and simulation of targets but are typically comprised of pristine samples that may not be representative of real world targets. This paper will provide methods, data and trends of the BRDF variability in the infrared regions. We will also explore appropriate data sets for use to represent typical fielded targets.
The current state of the art in synthetic radiometrically accurate scene generation for visual signatures remains immature. Even more difficult is creating composite images of photo-realistic synthetic images placed into images of real scenes. A potential solution to this problem is to use measured background data to drive the target rendering process. This approach has the advantage of deriving synthetic images with sufficient fidelity for inputs into the visual laboratory and performance codes. Since scene luminance can change rapidly, especially during partly cloudy conditions, all measurements must be obtained nearly simultaneously. This paper will explore the requirements for a visual predictive code and meeting these requirements with a background measurement process. A prototype measurement system will be described along with results from measurements.
There is a push in the Army to develop lighter vehicles that can get to remote parts of the world quickly. This objective force is not some new vehicle, but a whole new way of fighting wars. The Future Combat System (FCS), as it is called, has an extremely aggressive timeline and must rely on modeling and simulation to aid in defining the goals, optimizing the design and materials, and testing the performance of the various FCS systems concepts. While virtual prototyping for vehicles (both military and commercial) has been around as a concept for well over a decade and its use is promoted heavily in tours and in boardrooms, the actual application of virtual protoyping is often limited and when successful has been confined to specific physical engineering areas such as weight, space, stress, mobility, and ergonomics. If FCS is to succeed in its acquisition schedule, virtual prototyping will have to be relied on heavily and its application expanded. Signature management is an example of an area that would benefit greatly from virtual prototyping tools. However, there are several obstacles to achieving this goal. To rigorously analyze a vehicle's IR and visual signatures extensively in several different environments over different weather and seasonal conditions could result millions of potentially unique signatures to evaluate. In addition, there is no real agreement on what evaluate means or even what value is used to represent signature; Delta T( degree(s)C), Probability of Detection? What the user really wants to know is: how do I make my system survivable? This paper attempts to describe and then bound the problem and describe how the Army is attempting to deal with some of these issues in a holistic manner using SMART (Simulation and Modeling for Acquisition, Requirements, and Training) principles.
The spatial and spectral characteristics of targets and backgrounds must be known and understood for a wide variety of reasons such as: synthetic scene simulation and validation; target description for modelling; in- service target material characterisation and background variability assessment. Without this information it will be impossible to design effective camouflage systems and to maximise the capabilities of new sensors. Laboratory measurements of background materials are insufficient to provide the data required. A series of trials are being undertaken in the UK to quantify both diurnal and seasonal changes of a terrain background, as well as the statistical variability within a scene. These trials are part of a collaborative effort between the Defence Evaluation and Research Agency (UK), Defence Clothing and Textile Agency (UK) and the T.A.C.O.M. (USA). Data are being gathered at a single site consisting primarily of south facing mixed coniferous and deciduous woodland, but also containing uncultivated grassland and tracks. Ideally each point in the scene needs to be characterized at all relevant wavelengths but his is unrealistic. In addition there are a number of important environmental variables that are required. The goal of the measurement programme is to acquire data across the spectrum from 0.4 - 14 microns. Sensors used to include visible band imaging spectroradiometers, telespectroradiometers (visual, NIR, SWIR and LWIR), calibrate colour cameras, broad band SWIR and LWIR imagers and contact reflectance measurement equipment. Targets consist of painted panels with known material properties and a wheeled vehicle, which is in some cases covered with camouflage netting. Measurements have bene made of the background with and without the man- made objects present. This paper will review the results to date and present an analysis of the spectral characteristics fo different surfaces. In addition some consideration will be given to the implications of the data obtained for camouflage design.
The application of advanced low observable treatments to ground vehicles has led to a requirement for a better understanding of effects of light scattering from surfaces. Measurements of the Bidirectional Reflectance Distribution Function (BRDF) fully describe the angular scattering properties of materials, and these may be used in signature simulations to quantitatively characterize the optical effects of surface treatments on targets. This paper reviews the theoretical and experimental techniques for characterizing the BRDF of surfaces and examines some of the popular parameterized BRDF representations that are used in signature calculations.
Proc. SPIE. 3375, Targets and Backgrounds: Characterization and Representation IV
KEYWORDS: Near infrared, Data modeling, Visualization, Reflectivity, Bidirectional reflectance transmission function, Modeling and simulation, Statistical modeling, Performance modeling, Systems modeling, Process modeling
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.
Improvements in the fidelity of predictive computer models have brought requirements for more robust reflectance modeling. These requirements have focused new interest in measurement processes and data representation. Representation of the data is of critical importance to rendering models such as ray tracers and radiance renders. In these cases concise and accurate reflectance representation drives speed performance of the modeling. Many types of reflectance representations exist but the bidirectional reflectance is the most general case, from which all the others can be derived. This paper explores the bidirectional reflectance function, its measurement techniques and linkages into predictive modeling. Limitations to each of these areas will also be discussed.
The technique for calibrating color imagery which has been employed by the Tank-Automotive Research Development and Engineering Center (TARDEC) includes measurement of red, green, and blue color panels using a colorimeter during the approximate time that the calibration image is captured. This method has the advantage that the luminance and chromaticity coordinates of the color panels are recorded in real time. However, the disadvantage is the amount of time it takes to measure each individual panel. Outside of a laboratory, the environment cannot be controlled, so the light level and correlated color temperature from the source may shift during the calibration period. A new technique using a spectroradiometer has been developed whereby the spectral reflectance of the color panels are measured beforehand and only the light level and spectral content from the source is monitored during the calibration period. This drastically reduces the time required for calibration, thus rendering insignificant any temporal changes in the light level or correlated color temperature of the panels. The actual luminance and chromaticity of the color panels can be calculated subsequently.