Resampling analysis of participant variance to improve the efficiency of sensor modeling perception experiments

John D. O'Connor; Jonathan Hixson; Patrick McKnight; Matthew S. Peterson; Raja Parasuraman

doi:10.1117/12.851018

22 April 2010 Resampling analysis of participant variance to improve the efficiency of sensor modeling perception experiments

John D. O'Connor, Jonathan Hixson, Patrick McKnight, Matthew S. Peterson, Raja Parasuraman

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Proceedings Volume 7662, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXI; 76620L (2010) https://doi.org/10.1117/12.851018
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Night Vision and Electronic Sensors Directorate (NVESD) Modeling and Simulation Division (MSD) sensor models, such as NV Therm IP, are developed through perception experiments that investigate phenomena associated with sensor performance (e.g. sampling, noise, sensitivity). A standardized laboratory perception testing method developed in the mid-1990's has been responsible for advances in sensor modeling that are supported by field sensor performance experiments.1 The number of participants required to yield dependable results for these experiments could not be estimated because the variance in performance due to participant differences was not known. NVESD and George Mason University (GMU) scientists measured the contribution of participant variance within the overall experimental variance for 22 individuals each exposed to 1008 stimuli. Results of the analysis indicate that the total participant contribution to overall experimental variance was between 1% and 2%.

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John D. O'Connor, Jonathan Hixson, Patrick McKnight, Matthew S. Peterson, and Raja Parasuraman "Resampling analysis of participant variance to improve the efficiency of sensor modeling perception experiments", Proc. SPIE 7662, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXI, 76620L (22 April 2010); https://doi.org/10.1117/12.851018

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KEYWORDS

Sensors

Performance modeling

Sensor performance

Electro optical modeling

Visual process modeling

Error analysis

Modeling and simulation

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