Kernel Density–Based Algorithm for Despiking ADV Data
Publication: Journal of Hydraulic Engineering
Volume 139, Issue 7
Abstract
Acoustic doppler velocimeter (ADV) data can be contaminated by spikes from various sources. Available despiking methods were found to encounter difficulties in despiking ADV data from a turbulent jet flow. An iteration-free despiking algorithm was developed for highly contaminated ADV data by applying a bivariate kernel density function and its gradient to separate the data cluster from the spike clusters. It is shown that the new method overcomes some of the deficiencies of the existing despiking methods.
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Acknowledgments
The first author received funding from the NSERC postgraduate scholarship and Alberta Ingenuity scholarship.
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Information & Authors
Information
Published In
Journal of Hydraulic Engineering
Volume 139 • Issue 7 • July 2013
Pages: 785 - 793
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jun 23, 2010
Accepted: Jan 2, 2013
Published online: Jan 4, 2013
Published in print: Jul 1, 2013
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