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This document discusses a method for predicting future compliance outcomes in GTEM (Gigahertz Transverse Electromagnetic) testing. It outlines the challenges posed by site-to-site differences, including calibration variations, tester preferences, and external factors like weather. The proposed approach focuses on generating predictive data to facilitate pre-compliance testing, moving beyond correlation measurements. By accounting for variations and offering a linear gauge of unit performance, the method promises improved reliability in compliance predictions, with potential applications across various tests.
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Predicting the Future The GTEM “Tamed?” Mathew Aschenberg Derrell Gottwald Charles Grasso Echostar TechnologyEchostar Technology Echostar Technology Agency Engineer Quality Assurance Engineer Senior Agency Engineer (303)706-5064 (303) 706-5236 (303) 706 – 5467 Email: matt.aschenberg@echostar.com Email: derrell.gottwald@echostar.com Email:charles.grasso@echostar.com
Pre-Compliance Testing • Will It Pass? PREDICTION Not CORRELATION
Site to Site Differences Acceptable correlation differences Calibration variation Tester differences/preferences Weather Voltage supplied (120 vs. 115) Measurement Uncertainty vs. Correlation
Measurement Uncertainty vs. Correlation EUT Variation • Cable Layout • System state • Seam contact
Benefits of Process • Accounts for site differences • Provides a linear gauge of unit performance • Self-Reinforcing - Improves with time • Transferable to other tests
Future Work • Incorporate frequency specific statistics • Account for inter-relatedness between emissions
GTEM Overview • 50Ω transmission line • 3-orthogonal axis measurements • Spherical not cylindrical measurements • Size limited • Cables present problems
Summary: • Correlation between OATS and non-traditional sites can be difficult to achieve. • A method is proposed that accounts for site differences and provides for prediction instead of correlation. • Using this method a GTEM, for example, can be used to provide useful pre-compliance data.