Real-Time Target Motion Animation for Missile Warning System Testing

Perkins, Timothy; Sundberg, Robert; Cordell, John; Tun, Zaw; Owen, M.

doi:10.21236/ada640003

Cited by 2 publications

(1 citation statement)

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“…To investigate spectral signature variation for vehicles, we modified our Quick Image Display (QUID) model 6,7 to generate effective BRDF-based spectra for the whole object as well as the object at a desired pixel resolution. The inputs required include the spectral reflectivity of the materials making up the exterior of the object, a geometric shape description, typically as a 3D wireframe or facet model, and a model of the environmental illumination.…”

Section: Vehicle Simulation Methodsmentioning

confidence: 99%

Spectral BRDF modeling of vehicle signature observations in the VNIR-SWIR

et al. 2016

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Hyperspectral imaging (HSI) sensors have the ability to detect and identify objects within a scene based on the distinct attributes of their surface spectral signatures. Many targets of interest, such as vehicles, represent a complex arrangement of specular (non-Lambertian) materials with curved and flat surfaces oriented at varying view factors. This complexity, combined with possible changing atmospheric/illumination conditions and viewing geometries, can produce significant variations in the observed signatures from measurement to measurement, making detection and/or reacquisition challenging. This paper focuses on the characterization of visible-near infrared-short wave infrared (VNIR-SWIR) spectra for detection, identification and tracking of vehicles. Signature variations are predicted using a novel image simulation tool to calculate spectral images of complex 3D objects from a spectral material description such as the modified Beard-Maxwell BRDF model, a wireframe shape model, and a directional model of the illumination. We compare the simulations with recent VNIR-SWIR hyperspectral imagery of vehicles and panels collected at the Rochester Institute of Technology during an Autumn 2015 measurement campaign. Variations in both the simulated and measured spectra arise mainly from differences in the relative glint contribution. Implications of these variations on vehicle detection and identification are briefly discussed.

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Section: Vehicle Simulation Methodsmentioning

confidence: 99%