Simulation-based sensor modeling and at-range target detection characterization with MuSES

Corey D. Packard; Allen R. Curran; Nicholas E. Saur; Peter L. Rynes

doi:10.1117/12.2177310

12 May 2015 Simulation-based sensor modeling and at-range target detection characterization with MuSES

Corey D. Packard, Allen R. Curran, Nicholas E. Saur, Peter L. Rynes

Author Affiliations +

Proceedings Volume 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI; 94520F (2015) https://doi.org/10.1117/12.2177310
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Accurate infrared signature prediction of targets, such as humans or ground vehicles, depends primarily on the realistic prediction of physical temperatures. Thermal model development typically requires a geometric description of the target (i.e., a 3D surface mesh) along with material properties for characterizing the thermal response to simulated weather conditions. Once an accurate thermal solution has been obtained, signature predictions for an EO/IR spectral waveband can be generated. The image rendering algorithm should consider the radiative emissions, diffuse/specular reflections, and atmospheric effects to depict how an object in a natural scene would be perceived by an EO/IR sensor. The EO/IR rendering process within MuSES, developed by ThermoAnalytics, can be used to create a synthetic radiance image that predicts the energy detected by a specific sensor just prior to passing through its optics. For additional realism, blurring due to lens diffraction and noise due to variations in photon detection can also be included, via specification of sensor characteristics. Additionally, probability of detection can be obtained via the Targeting Task Performance (TTP) metric, making it possible to predict a target’s at-range detectability to a particular threat sensor. In this paper, we will investigate the at-range contrast and detectability of some example targets and examine the effect of various techniques such as sub-pixel sampling and target pixel thresholding.

Citation Download Citation

Corey D. Packard, Allen R. Curran, Nicholas E. Saur, and Peter L. Rynes "Simulation-based sensor modeling and at-range target detection characterization with MuSES", Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 94520F (12 May 2015); https://doi.org/10.1117/12.2177310

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KEYWORDS

Sensors

Target detection

Modulation transfer functions

Image sensors

Palladium

Diffraction

Thermography

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