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The accuracy of modelled performance data for EO/IR sensors is often limited by the accuracy with which image processing functions can be represented in system-level models and simulations. This is particularly so for those cases where complex processing functions are required, such as those found in autonomous ATD/R systems. Furthermore, for sensors mounted on moving platforms, variability in the frame-to-frame image quality can dominate the achieved measures of performance and effectiveness during an engagement. An established technique to address this involves the use of image-based simulations which process dynamically changing imagery using representative image processing functions. However, such an approach requires extensive run-times and a large volume of real or synthetic image data, both of which can be prohibitive. An alternative approach is presented here whereby a limited number of images are processed and then used to generate statistically based performance transfer functions using an appropriate interpolation scheme. These transfer functions are then used to represent the output response of the processing chain when the received imagery is subjected to different levels of degradations such as distortion and blurring. Such transfer functions can then be stored in multidimensional look-up tables which can be rapidly accessed by a system-level Monte-Carlo performance simulation. The ability to represent and extract the performance-related transfer functions is dependent upon the image quality metrics and the accuracy of the corresponding parametric model requires careful consideration of the model validation. An example simulation is presented based on an autonomous ATD/R sensor system mounted on an airborne platform. The importance of validation is demonstrated, and the increased run-time benefits are described. The proposed parametric image modelling approach provides sensor system designers with increased confidence in their design and compliance, and this helps reduces the early design risk.
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