The Fraunhofer thermal object model (FTOM) predicts the temperature of an object as a function of the environmental conditions. The model has an outer layer exchanging radiation and heat with the environment and a stack of layers beyond modifying the thermal behavior. The orientation of the layer is defined by the normal vector of the surface. The innermost layer is at a constant or variable temperature called core temperature. All the layers have heat capacity and thermal conductivity. The outer layers properties are color (visible), emissivity (IR), coefficients of free and forced convection, and a factor for latent heat. The environmental parameters are air temperature, wind speed, relative humidity, irradiation of the sun, and thermal radiation of the sky and ground. The properties of the model (7 parameters) are fitted to minimize the difference between the prediction and a time series of measured temperatures. The size of the time series is one or more days with 288 values per day (5 minute resolution). The model is useable for very different objects like backgrounds (meadow, forest, rocks, sand, or bricks) or parts of objects like vehicles. The STANDCAM is a decoy of a vehicle and is used to constitute a thermal signature and is not classified. The STANDCAM has a complex CAD-Model with thousands of triangular facets that had to be simplified for the thermal simulation. The CAD model was available through WTD 52, an agency of the Federal Office of Bundeswehr Equipment, Information Technology and In-Service Support (BAAINBw). Groups of elements of the model facing in the same direction and behaving similarly were cut out and grouped in distinct objects. The calculation of the temperature of the objects is based on measured environmental data and the model parameters are fitted on measured radiation temperatures of the objects and backgrounds. For the visualization the object is surrounded by a world texture. For the radiation temperature of the environment and the ground under the object measured air and meadow temperatures were used. The temperature is coded as a color from a palette (here we use a grey palette) and is updated regularly throughout the calculation of the scene for the selected view and is stored as a texture bitmap. The animation of the temperature textures is directly performed by BLENDER.The result of the visualization is available as movie that is watchable in real time or time lapse.
In this work an adaptive panel in the visible spectral range is presented. Principal possibilities and basic aspects of adaptive camouflage in the VIS are considered and some details are discussed. The panel consists of modular tiles, each containing several high power four-color-LEDs controlled by a microcontroller and high current power supply and each tile designed to operate autonomously. To control the color and the intensity several color sensors were integrated into the system. The purpose of the panel is to take on a uniform color to best match its appearance to a given reference color, where both the panel and the reference color are subject to the same environmental conditions. The panel was not designed, however, to produce different camouflage patterns. The tiles on the surface were covered by a dark plastic plate in order to provide dark and saturated colors and to guarantee a dark appearance in the passive state of the system. As was to be expected, extreme situations like high ambient brightness and direct solar illumination turned out to be particularly challenging. Substantial tests and some modifications were performed to achieve a satisfactorily uniform color reproduction of a given reference color. Physical measurements as well as observer tests have been performed to demonstrate the capability of the adaptive system.
The Fraunhofer thermal object model (FTOM) predicts the temperature of an object as a function of the environmental conditions. The model has an outer layer exchanging radiation and heat with the environment and a stack of layers beyond modifying the thermal behavior. The innermost layer is at a constant or variable temperature called core temperature. The properties of the model (6 parameters) are fitted to minimize the difference between the prediction and a time series of measured temperatures. The model can be used for very different objects like backgrounds (e.g. meadow, forest, stone, or sand) or objects like vehicles.
The two dimensional enhancement was developed to model more complex objects with non-planar surfaces and heat conduction between adjacent regions. In this model we call the small thermal homogenous interacting regions thermal pixels. For each thermal pixel the orientation and the identities of the adjacent pixels are stored in an array. In this version 7 parameters have to be fitted. The model is limited to a convex geometry to reduce the complexity of the heat exchange and allow for a higher number of thermal pixels.
For the test of the model time series of thermal images of a test object (CUBI) were analyzed. The square sides of the cubes were modeled as 25 thermal pixels (5 × 5). In the time series of thermal images small areas in the size of the thermal pixels were analyzed to generate data files that can easily be read by the model.
The program was developed with MATLAB and the final version in C++ using the OpenMP multiprocessor library. The differential equation for the heat transfer is the time consuming part in the computation and was programmed in C. The comparison show a good agreement of the fitted and not fitted thermal pixels with the measured temperatures. This indicates the ability of the model to predict the temperatures of the whole object.