Proc. SPIE. 5431, Targets and Backgrounds X: Characterization and Representation
KEYWORDS: Target detection, Detection and tracking algorithms, Data modeling, Phase modulation, Sensors, Databases, Analytical research, Modeling and simulation, Infrared signatures, Received signal strength
A segment of the modeling and simulation community and key decision makers still hold to the misconception that a vehicle can have a single or a representative thermal "signature" for a given scenario-such as daytime summer or night time summer. In truth, a vehicle in a "daytime summer Northeast Asia" scenario can manifest many different types of detectabilities and signature manifestations throughout the day and under differing weather conditions. A reasonable approach toward representing a vehicle's signature characteristics would be to understand that data spread and choose the best value or values that address the question asked of a particular simulation. The Army Materiel Systems Analysis Activity (AMSAA) is moving towards addressing this problem and is seeking to use modeling and Simulation (M&S) tools to populate its databases in a reasonable manner. Using the latest M&S tools, the authors will present unclassified results of measurements and simulations demonstrating this data spread and the resulting CASTFOREM sensitivity analysis. Images and the Delta T-RSS metric will be used to demonstrate the concept of the data distribution. By moving toward the signature data spread mentality, the research and development community can help the sensor and operations community pick the appropriate values for particular analyses--even for vehicles that are in the concept design phase.
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.
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.
This paper describes the initial phase of an evaluation study on the performance of PMO, the Paint Map Optimizer, for long wave infrared (LWIR) modeling. In this phase, we will evaluate using PRIMS, the Physically Reasonable Infrared Signature Modeler, to predict the thermal signature of a simplified tank geometry, and then PMO to predict the optimal thermal camouflage pattern from a range of emissivities in a given scenario. Prism is a thermal modeling code that has been used extensively to model thermal signatures of military ground vehicles. PMO was developed by Aerodyne Research to provide a computer-aided design tool for camouflage pattern design and optimization in a given scenario and a given band for the US Army Aviation Technology Directorate, AATD. At the end of this phase, we hope to determine the basic effectiveness of the process and identify areas of improvement if necessary. The geometry was modeled in PRISM. which output the thermal signature for input into PMO. The optimizer was used to predict the thermal camouflage pattern in the 8-12micrometers IR band for a range of emissivities with the geometry in three different locations in the background image.
Visual and IR imaging of rotating objects, such as tires under load test conditions is desirable but presents several obstacles, such as blurring and component obstruction. If an imaging system capable of high enough frame rates to capture the data without aliasing effects is used, the entire object is often not in view and therefore could not be analyzed. The authors present a system that uses a standard Inframetrics 760 to synthetically reconstruct an image by using high speed imaging techniques in conjunction with a shaft encoder, rotational tracking, and specialized data sampling software to capture three sides of a tire that is being tested at 50 mph. This process has proven valuable in providing non-intrusive thermal analysis in recent quality tests of tires. Current accepted testing techniques for tires include endurance test and x-ray. Both provide limited information, but neither provides the thermal information that is a dominant factor in tire failure. By setting the IR camera in a position to face the edge of the tire, using mirrors to bring the sidewall into the field of view, and using our methodology we have collected data on the entire tire under two minutes. This data can be viewed as flat data array images or rendered onto a 3D wireframe representation.
This paper reviews current and future signature modeling activities at KRC and TACOM. PRISM (Physically Reasonable Infrared Signature Model) and its associated modeling tools are discussed along with the implementation of the physical principles that will evolve into the SuperCode. By continuing the current efforts with PRISM and then forming a SuperCode Research Consortium to implement additional advanced features, a universal code will be available to the modeling community.
For years, thermal model developers have promoted the approach of using simulated data validated by measurements as the best method of analyzing all aspects of a thermal signature -- target, background, atmosphere, and imager. Recent advances in high speed CPUs and high performance graphic workstations have allowed for improved proficiency in these thermal signature models and has helped convince `the user' that the modeled approach is viable. Now, targets and backgrounds can be modeled more quickly and with better realism and imagers of all types can be simulated in practical runtimes. These improved capabilities increase the temptation to look to modeling as the panacea for all difficulties encountered in infrared imaging as sensor designers, smart weapons designers, and vehicle concept designers all realize the cost and practical limitations of using measured data only. This paper examines the benefits and pitfalls experienced by U.S. Army modelers particularly in the target and background modeling area and provides some guidelines for future modeling directions.
The infrared exitance of steel plates with several emissivities are modeled using PRISM 3.0 and LOWTRAN7 under sky backgrounds representative of Middle East desert conditions in the summer. LOWTRAN7 is used to calculate the downward thermal radiance of a desert haze atmosphere with multiple scattering. PRISM 3.0 incorporates the results from LOWTRAN7 into annular rings that represent the temperature gradiant of the sky dome and predicts the apparent temperature of the plates in the 8 to 14 micron band. This study is part of a preliminary look at the issue of passive low observable technology for application to ground vehicles and an illustration of state-of-the-art computer-based background modeling and thermal simulation.