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3 May 2016 An imaging system detectivity metric using energy and power spectral densities
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The purpose of this paper is to construct a robust modeling framework for imaging systems in order to predict the performance of detecting small targets such as Unmanned Aerial Vehicles (UAVs). The underlying principle is to track the flow of scene information and statistics, such as the energy spectra of the target and power spectra of the background, through any number of imaging components. This information is then used to calculate a detectivity metric. Each imaging component is treated as a single linear shift invariant (LSI) component with specified input and output parameters. A component based approach enables the inclusion of existing component-level models and makes it directly compatible with image modeling software such as the Night Vision Integrated Performance Model (NV-IPM). The modeling framework also includes a parallel implementation of Monte Carlo simulations designed to verify the analytic approach. However, the Monte Carlo simulations may also be used independently to accurately model nonlinear processes where the analytic approach fails, allowing for even greater extensibility. A simple trade study is conducted comparing the modeling framework to the simulation.
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Bradley L. Preece, David Haefner, and Georges Nehmetallah "An imaging system detectivity metric using energy and power spectral densities", Proc. SPIE 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII, 98200Q (3 May 2016);

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