SigSimRT and SenSimRT are distributed as a dll based SDK with highly configurable parameterization. The component based interfaces do allow you to intercept the SDK contributions before applying them to the graphics shaders, textures and uniforms.

SigSimRT generates uniforms and textures which will be used in the pixel shaders. The pixel shaders then perform the rendering, and will need to be modified by the user to call JRM signature generation shader functions that are provided as part of the SDK.

It comes with notional settings for Visible Camera, EO, NVG, MWIR and LWIR cameras. Unfortunately, specific sensor settings would take this from EAR to ITAR.

Typically, raw satellite and/or feature imagery is imported into JRM’s GenesisMC for material classification at the texture level. The scene geometry, textures, and material files are loaded at startup, and SigSim libraries are called to provide material spectral data, compute ephemeris and spectral irradiances, resulting temperature solution parameters, and atmospheric parameters. GPU shaders then put the final at-aperture signature together, and pass this 2D radiance image to SenSim, which adds physics-based, user-defined sensor effects.

Some of the example images on the JRM website contain much less noise than often appears in real IR images. This is so that we don’t obscure the presentation of other important effects like angle-dependent thermal loading, BRDF reflection calculation, the quality and resolution of our terrain and entity texturing, special effects, and so forth. In general, the amount of sensor noise and blur are governed by physics calculations based on user input of optics, detector, and electronics parameters, such as aperture size and shape, focal length, detector NEDT, 1/f noise exponent and knee frequency, etc. In 2005, JRM’s products were evaluated against non-real-time government codes such as MUSES, SPIRITS, and MODTRAN and found to be accurate to within 2% radiometrically.

Yes. Currently over 300 measured but encrypted material files (.MTLE) are shipped, each containing both bulk properties (for thermal calculations) and spectral surface BRDF properties. However, the software will read encrypted material files (.MTL) as well, and this file format is open, so that users may define their own materials. These material files are placed in the same directory as the rest of the materials, and are referred to by name in the definition of material systems, which are layered 1D stacks of raw materials, with user-defined thermal boundary conditions at the topmost/outermost and bottom most/innermost layer. This unique “material system” concept allows for proper modeling of thermal inertia in the presence of irradiance and convective loading. The material system files (each containing the set of material systems applicable to a given texture or set of textures) can be placed anywhere on disk, and are loaded when the applicable terrain or 3D feature/entity model is loaded. JRM products ship with default sets of both MTL and MS files.

Yes. We support four AGC models, including Histogram Equalization and Histogram Projection, with user-specified minimum and maximum gain limits.

Yes, JRM has a product (“OSV-Radar”) for predicting radar imagery of various formats (SAR, ISAR, MTI, RBGM, etc.), using the same materially- encoded terrain and entity models as for other bands. The material files contain areal RCS parameters for a variety of RF bands. User inputs include area of interest, average power, gain, PRF, pulse width, RF center frequency, and of course the environmental/atmospheric controls.

The spatial resolution is governed by the user- defined input parameters for such quantities as airspeed, pulse width, PRF, and wavelength, with a lower limit at the spatial resolution of the underlying material-encoded terrain texture.

Yes, the radar module uses the same materially-classified scene database as for the EO/IR bands. The accuracy of the GenesisMC material classification is governed by: (a) The number and variety of different measured material data against which the input image is spectrally matched. Here JRM has a clear advantage over other material classifiers, as we are able to measure our own materials, and have a stock of over 300 different materials to choose from currently. Other classifiers only classify to the level of broad material category, like rock, water, grass, and soil. We offer detail down to the level of individual rock, soil, and vegetation species; (b) The spectral resolution (number of channels) of the input imagery. Again, JRM has an advantage in that our classifier can take advantage of an unlimited number of spectral bands; and (c) The availability of shapefile data, which is an industry-standard parameterized format allowing for pre-regioning of an area-of-interest to specifically denote the location of road networks and building footprints. JRM is currently implementing support for shapefile import.

Absolutely. In addition to providing a full Modtran/Radtran interface, JRM’s products contain ephemeris models for predicting apparent solar/lunar/stellar positions in the sky, an irradiance model for predicting the spectral direct and diffuse irradiance on surfaces from these sources, through the defined atmosphere, and a fully-transient finite-difference thermal solver for predicting temperature based on this irradiance. In fact, JRM’s unique parameterization of the thermal solution feeds GPU shaders with coefficients used in an on-the-fly compilation of angle-dependent solar loading based on the specific surface normal encountered during rendering.