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6 October 2007 Validation of a target acquisition model for active imager using perception experiments
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Active night vision systems based on laser diodes emitters have now reached a technology level allowing military applications. In order to predict the performance of observers using such systems, we built an analytic model including sensor, atmosphere, visualization and eye effects. The perception task has been modelled using the Targeting Task Performance metric (TTP metric) developed by R. Vollmerhausen from the Night Vision and Electronic Sensors Directorate (NVESD). Sensor and atmosphere models have been validated separately. In order to validate the whole model, two identification tests have been set up. The first set submitted to trained observers was made of hybrid images. The target to background contrast, the blur and the noise were added to armoured vehicles signatures in accordance to sensor and atmosphere models. The second set of images was made with the same targets, sensed by a real active sensor during field trials. Images were recorded, showing different vehicles, at different ranges and orientations, under different illumination and acquisition configurations. Indeed, this set of real images was built with three different types of gating: wide illumination, illumination of the background and illumination of the target. Analysis of the perception experiments results showed a good concordance between the two sets of images. The calculation of an identification criterion, related to this set of vehicles in the near infrared, gave the same results in both cases. The impact of gating on observer's performance was also evaluated.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Frédéric Lapaz and Loïc Canevet "Validation of a target acquisition model for active imager using perception experiments", Proc. SPIE 6737, Electro-Optical and Infrared Systems: Technology and Applications IV, 67370G (6 October 2007);

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