Kernel Density–Based Algorithm for Despiking ADV Data

• Published in: Journal of hydraulic engineering (New York, N.Y.) Vol. 139; no. 7; pp. 785 - 793
• Main Authors: Islam, Md Rashedul, Zhu, David Z
• Format: Journal Article
• Language: English
• Published: Reston, VA American Society of Civil Engineers 01.07.2013
• Subjects: Algorithms; Clusters; Contamination; Density; Exact sciences and technology; Fluid dynamics; Fundamental areas of phenomenology (including applications); Instrumentation for fluid dynamics; Kernels; Physics; Spikes; Technical Note; Technical Notes; Turbulence; Velocimetry; Data analysis; Turbulent flow; Hydraulics; Acoustic techniques; Despiking; Doppler effect; Acoustic method; Outlier; Acoustic doppler velocimeter (ADV); Algorithms; Kernel density; Jet; Jets; Velocimeters; Velocity measurement
• ISSN: 0733-9429; 1943-7900
• DOI: 10.1061/(ASCE)HY.1943-7900.0000734

AbstractAcoustic 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.