No abstract

No abstract

No abstract

No abstract

In general, questions of model credibility introduce more problems in a generic model than they do in models developed for one specific application since a generic formulation must allow for many applications of the model. This paper addresses the issues of model testing, verification, and validation for a generic electro-optic sensor system model. A structural approach to testing, verification, and validation is proposed that builds increasing confidence through bottomup testing, structured verification procedures, and carefully selected validation metrics. These metrics are based on a geometrical view of model outputs that may be compared with measurements using qualitative methods or quantitative approaches involving image processing, artificial neural networks, or fuzzy pattern recognition. The advantage over traditional validation methods is most marked in the case of complex models with many key quantities where it not only provides insight about the validity but also about sensitivities. These validation tools have been applied, in conjunction with more traditional metrics, to the testing, verification, and validation of the generic model configured as a thermal imager system.