Imaging system design and performance depend upon a myriad of radiometric, spectral, and spatial parameters. The “bare bones” sensor consists of optics, detector, display, and an observer. Range degrading parameters include 3D noise, optical blur, and pixel interpolation. Scenario parameters include detection, recognition, and identification probability, target contrast, target size, line-of-sight motion, and atmospheric conditions. Generally, the customer provides the scenario and the analyst optimizes sensor parameters to achieve maximum acquisition range. A wide variety of programs have been available in the past (e.g., SSCamIP, NVThermIP etc.). These programs have been consolidated into the Night Vision Integrated Performance Model (NVIPM). For convenience, the calculations are performed in the frequency domain (MTF analysis). This is often called image chain modeling. Although the math is sometimes complex, the equations are graphed for easy interpretation. NVIPM can easily perform trade studies and provides a gradient (sensitivity) analysis. Gradient analysis lists those parameters (in decreasing order) that affect acquisition range.
This course consists of 6 sections: (1) The history of imaging system design and the transition from scanning arrays to staring arrays, (2) imaging system chain analysis covering MTF theory, “bare bones” system design, environmental effects (atmospheric attenuation, turbulence, and line-of-sight motion a.k.a. jitter), sampling artifacts, and image processing, (3) detector responsivity, radiometry, various noise sources (photon, dark current, read) and the resulting SNR, (4) targets, backgrounds, and target signatures, (5) various image quality metrics which includes NVIPM, and (6) acquisition range and trade studies. By far, the most important section is the trade study graphical representations. Three optimization examples are provided (case study examples): long range imaging, short range imaging, and IRST systems.
While the course emphasizes infrared system design, it applies to visible, NIR, and short infrared (SWIR) systems. From an optimization viewpoint, the only difference across the spectral bands is the target signature nomenclature. When considering hardware design, the spectral region limits lens material and detector choices.
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ONLINE COURSES
Imaging system design and performance depend upon a myriad of radiometric, spectral, and spatial parameters. The “bare bones” sensor consists of optics, detector, display, and an observer. Range degrading parameters include 3D noise, optical blur, and pixel interpolation. Scenario parameters include detection, recognition, and identification probability, target contrast, target size, line-of-sight motion, and atmospheric conditions. Generally, the customer provides the scenario and the analyst optimizes sensor parameters to achieve maximum acquisition range. A wide variety of programs have been available in the past (e.g., SSCamIP, NVThermIP etc.). These programs have been consolidated into the Night Vision Integrated Performance Model (NVIPM). For convenience, the calculations are performed in the frequency domain (MTF analysis). This is often called image chain modeling. Although the math is sometimes complex, the equations are graphed for easy interpretation. NVIPM can easily perform trade studies and provides a gradient (sensitivity) analysis. Gradient analysis lists those parameters (in decreasing order) that affect acquisition range.
This course consists of 6 sections: (1) The history of imaging system design and the transition from scanning arrays to staring arrays, (2) imaging system chain analysis covering MTF theory, “bare bones” system design, environmental effects (atmospheric attenuation, turbulence, and line-of-sight motion a.k.a. jitter), sampling artifacts, and image processing, (3) detector responsivity, radiometry, various noise sources (photon, dark current, read) and the resulting SNR, (4) targets, backgrounds, and target signatures, (5) various image quality metrics which includes NVIPM, and (6) acquisition range and trade studies. By far, the most important section is the trade study graphical representations. Three optimization examples are provided (case study examples): long range imaging, short range imaging, and IRST systems.
While the course emphasizes infrared system design, it applies to visible, NIR, and short infrared (SWIR) systems. From an optimization viewpoint, the only difference across the spectral bands is the target signature nomenclature. When considering hardware design, the spectral region limits lens material and detector choices.
