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Efficiency of Sequential Spatial Adaptive Sampling Algorithm to Accelerate Multi-Frequency Near-Field Scanning Measurement

Abstract : Near-field scanning is an attractive method to diagnose conducted and radiated emission issues at board or circuit level. However, the long measurement time to scan a dense electronic board at numerous frequencies is a major drawback that limits its growth in industry. To overcome this problem, methods to accelerate the measurement, such as the sequential spatial adaptive sampling algorithm, must be found. This method was developed to speed up a near-field scans measured in frequency domain at only one frequency. The proposed paper capitalizes on this method to extend its principle and demonstrate its effectiveness for near-field scans measured at multiple frequencies. Several measurement strategies and frequency segmentation approaches are compared. A definition of the best strategy and approach to allow a time reduction factor of almost one order magnitude compared to the full sampling measurement has been proposed.
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https://hal.laas.fr/hal-03543405
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Submitted on : Wednesday, January 26, 2022 - 8:27:20 AM
Last modification on : Friday, July 29, 2022 - 3:53:24 PM
Long-term archiving on: : Wednesday, April 27, 2022 - 6:14:06 PM

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Sébastien Serpaud, Alexandre Boyer, Sonia Ben Dhia, Fabio Coccetti. Efficiency of Sequential Spatial Adaptive Sampling Algorithm to Accelerate Multi-Frequency Near-Field Scanning Measurement. IEEE Transactions on Electromagnetic Compatibility, Institute of Electrical and Electronics Engineers, 2022, 64 (3), pp.816 - 826. ⟨10.1109/TEMC.2021.3136096⟩. ⟨hal-03543405⟩

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