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Evaluating Correlation Between Measurement Samples in Reverberation Chambers Using Clustering

Evaluating Correlation Between Measurement Samples in Reverberation Chambers Using ClusteringAbstract: Traditionally, in reverberation chambers (RC) measurement autocorrelation or correlation-matrix methods have been applied to evaluate measurement correlation. In this article, we introduce the use of clustering based on correlative distance to group correlated measurements. We apply the method to measurements taken in an RC using one and two paddles to stir the electromagnetic fields and applying decreasing angular steps between consecutive paddles positions. The results using varying correlation threshold values demonstrate that the method calculates the number of effective samples and allows discerning outliers, i.e., uncorrelated measurements, and clusters of correlated measurements. This calculation method, if verified, will allow non-sequential stir sequence design and, thereby, reduce testing time.Keywords: Correlation, Pearson correlation coefficient (PCC), reverberation chambers (RC), mode-stirring samples, correlative distance, clustering analysis, adjacency matrix.

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Updated: 2024-02-22
Metadata Last Updated: 2023-04-06 00:00:00
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Title Evaluating Correlation Between Measurement Samples in Reverberation Chambers Using Clustering
Description Evaluating Correlation Between Measurement Samples in Reverberation Chambers Using ClusteringAbstract: Traditionally, in reverberation chambers (RC) measurement autocorrelation or correlation-matrix methods have been applied to evaluate measurement correlation. In this article, we introduce the use of clustering based on correlative distance to group correlated measurements. We apply the method to measurements taken in an RC using one and two paddles to stir the electromagnetic fields and applying decreasing angular steps between consecutive paddles positions. The results using varying correlation threshold values demonstrate that the method calculates the number of effective samples and allows discerning outliers, i.e., uncorrelated measurements, and clusters of correlated measurements. This calculation method, if verified, will allow non-sequential stir sequence design and, thereby, reduce testing time.Keywords: Correlation, Pearson correlation coefficient (PCC), reverberation chambers (RC), mode-stirring samples, correlative distance, clustering analysis, adjacency matrix.
Modified 2023-04-06 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords Correlation , Pearson correlation coefficient (PCC) , reverberation chambers (RC) , mode-stirring samples , correlative distance , clustering analysis , adjacency matrix.
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