Sensor Simulation – Physics Based Imaging Sensors
SigSim – Radiometric Image Generation
By JRM Technologies
SigSim generates realistic sensor images for Out-the-Window (OTW), Computer Generated Forces (CGF), Semi-Automated Forces (SAF), and Hardware-In-The-Loop (HWIL) applications.
By applying advanced signature synthesis and atmospheric propagation models, SigSim’s ultra-fast algorithms credibily render the synthetic environment in any waveband within the 0.2 - 25.0um spectrum (UV, visible, near-IR, thermal-IR) and for arbitrary RF frequencies.
On-the-fly physics-based modeling and an open standards material classification approach make it an ideal for real-time sensor applications.
With SigSim, developers can upgrade their existing out-the-window Image generator (IG) or 3D simulation into a radiometrically-correct spectral sensor simulation. Additionally, SigSim can make your existing OTW simulations physically correct for striking realism.
SenSim – Sensor Device Modeling
SenSim models the detailed specifications of the sensing device and applies the correct effects to the imagery produced by SigSim.
SenSim is an advanced sensor modeling toolkit and run-time library for real-time sensor effects simulation of any optical sensor in the EO or IR passband. It provides engineering-level modeling of the optics, detector, electronics, and display components, simulating appropriate Modulation Transfer Functions (MTFs), detector sampling, noise, non-uniformity, dead-detectors, fill-factor, 1/f and white noise, pre-and post-amplifiers, and displays.
SenSim can use the actual sensor component specifications to provide the most realistic sensor visualization experience.
Genesis is an easy-to-use, GUI-based tool for high-confidence material-classification. Sensor simulations need to know what things are made of, not just what color they are. Genesis helps you map the textures and imagery in your synthetic environment to high fidelity material properties.
Its semi-automated approach speeds the process along, using smart reverse-signature predictive and spatial algorithms to predict the most likely surface material.