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Verification and validation issues in a generic model of electro-optic sensor systems

Smith, M.I., Murray-Smith, D.J. and Hickman, D. (2007) Verification and validation issues in a generic model of electro-optic sensor systems. Journal of Defense Modeling and Simulation, 4(1), pp. 17-27. (doi: 10.1177/875647930700400102)

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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 bottom-up 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.

Item Type:	Articles
Keywords:	Electro-optic sensor system, generic model, simulation, testing, verification, validation
Status:	Published
Refereed:	Yes
Glasgow Author(s) Enlighten ID:	Murray-Smith, Professor David
Authors:	Smith, M.I., Murray-Smith, D.J., and Hickman, D.
College/School:	College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:	Journal of Defense Modeling and Simulation
Journal Abbr.:	JDMS
Publisher:	SAGE
Copyright Holders:	Copyright © 2007 SAGE
First Published:	First published in Journal of Defense Modeling and Simulation 4(1)
Publisher Policy:	Reproduced in accordance with the copyright policy of the publisher

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References

1. Smith, M.I., Murray-Smith, D.J. and Hickman, D.: Mathematical and computer modelling of electro-optic systems using a generic modelling environment, manuscript submitted to Journal of Defense Modeling and Simulation, April 2006. 2. Murray-Smith, D.J., (1998), Methods for the external validation of continuous system simulation models: A review, Mathematical and Computer Modelling of Dynamical Systems, 4, 1, 5-31. 3. Smith, M.I.,(1999), The Design, Verification and Validation of a Generic Electro -optic Sensor Model for System Performance Evaluation, PhD Thesis, Department of Electronics and Electrical Engineering, University of Glasgow. 4. Smith, M.I., Hickman, D. and Murray-Smith, D.J., (1998),Test, verification and validation issues in modelling a generic electro-optic system, Proc. SPIE Annual Symposium, San Diego, SPIE Press, Washington. 5. Shakir, L.A., (1996), The complementary use of man-in-the-loop and closed-loop air combat models in weapon system effectiveness studies, Proc. Second Test and Evaluation International Aerospace Forum, Royal Aeronautical Society, London. 6. Pace, D.K., (1998), Verification, validation and accreditation. In Cloud, D.J. and Rainey, L.B. (eds.), Applied Modelling and Simulation: An integrated approach to development and operation, 369-409, McGraw-Hill, New York. 7. Kolence, K. and Kiviat, P., (1973), Software unit profiles and Kiviat figures, ACM SIGMETRICS, Pereformance Evaluation Review , Vol 2, No. 3, 2-12. 8. Tegarden, D.P., (1999), Business information visualization, Communications of the Association for Information Sciences, Vol. 1, Paper 4, pp 1-34. (http://cais.isworld.org/articles/1-4/default.asp?view=html&x=66&y=4). 9. Kamml, G., Voigt, H.-M. , Neß, K., (2005), Development of a tool to improve the forecast accuracy of dynamic simulation models for the paper process. In, Kappen, J. 18 and Manninen, J. (eds.), Cost Action E36, Modeling and Simulation in Pulp and Paper Industry, Proceedings of Model Validation Workshop, VTT Symposium 238, October 6th 2005, Espoo, Finland, VTT Research Centre of Finland, Espoo, Finland. 10. Rogers, S.K. and Kabrisky, M., (1991), An Introduction to Biological and Artificial Neural Networks for Pattern Recognition, SPIE Optical Engineering Press, Washington. 11. Sinha, D. and Giardina, C.R., (1990), Discrete black and white object recognition via morphological functions, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 12, No. 3, 275-293. 12. Ross, T.J., (1995), Fuzzy Logic with Engineering Applications, McGraw-Hill. 13. Anon, (1993) FLIR92 Thermal Imaging Systems Performance Model, US Army Night Vision and Sensors Directorate, Doc. RG009993.

Deposit and Record Details

ID Code:	66887
Depositing User:	Professor David Murray-Smith
Datestamp:	12 Jul 2012 15:10
Last Modified:	23 Sep 2021 05:22
Date of first online publication:	January 2007