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The use of mathematical models to predict the overall performance of an electro-optic (EO) system is well-established as
a methodology and is used widely to support requirements definition, system design, and produce performance
predictions. Traditionally these models have been based upon cascades of transfer functions based on established
physical theory, such as the calculation of signal levels from radiometry equations, as well as the use of statistical
models. However, the performance of an EO system is increasing being dominated by the on-board processing of the
image data and this automated interpretation of image content is complex in nature and presents significant modelling
challenges.
Models and simulations of EO systems tend to either involve processing of image data as part of a performance
simulation (image-flow) or else a series of mathematical functions that attempt to define the overall system
characteristics (parametric). The former approach is generally more accurate but statistically and theoretically weak in
terms of specific operational scenarios, and is also time consuming. The latter approach is generally faster but is unable
to provide accurate predictions of a system’s performance under operational conditions. An alternative and novel
architecture is presented in this paper which combines the processing speed attributes of parametric models with the
accuracy of image-flow representations in a statistically valid framework.
An additional dimension needed to create an effective simulation is a robust software design whose architecture reflects
the structure of the EO System and its interfaces. As such, the design of the simulator can be viewed as a software
prototype of a new EO System or an abstraction of an existing design. This new approach has been used successfully to
model a number of complex military systems and has been shown to combine improved performance estimation with
speed of computation.
Within the paper details of the approach and architecture are described in detail, and example results based on a practical
application are then given which illustrate the performance benefits. Finally, conclusions are drawn and comments given
regarding the benefits and uses of the new approach.
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