Compressive sensing for direct time of flight estimation in ultrasound-based NDT

R. Fuentes, K. Worden, I. Antoniadou, C. Mineo, S.G. Pierce, E.J. Cross

Research output: Contribution to conferencePaper

55 Downloads (Pure)

Abstract

This paper presents an approach for estimation of ultrasonic time-of-flight (TOF) within a Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) context. The presented method leverages recent advances in the field of Compressive Sensing (CS), which makes use of sparsity in a transform domain of a signal in order to reduce the number of samples required to store it. For this, the ultrasound signals are considered to be sparse in their autocorrelation domain and a method is suggested for building suitable basis functions, based on Hankel matrices, which transform a signal into its autocorrelation domain. It is shown how this can be combined with standard CS techniques in order to achieve very low error in TOF estimates with up to one-tenth of the original ultrasound samples.
Original languageEnglish
Number of pages12
Publication statusPublished - 12 Sep 2017
Event11th International Workshop on Structural Health Monitoring - Stanford, United States
Duration: 12 Sep 201714 Sep 2017
Conference number: 11
http://web.stanford.edu/group/sacl/workshop/IWSHM2017/

Conference

Conference11th International Workshop on Structural Health Monitoring
Abbreviated titleIWSHM 2017
CountryUnited States
CityStanford
Period12/09/1714/09/17
Internet address

Keywords

  • time-of-flight (TOF)
  • ultrasound pulses
  • non destructive testing
  • NDT
  • ultrasound signals

Fingerprint Dive into the research topics of 'Compressive sensing for direct time of flight estimation in ultrasound-based NDT'. Together they form a unique fingerprint.

  • Cite this

    Fuentes, R., Worden, K., Antoniadou, I., Mineo, C., Pierce, S. G., & Cross, E. J. (2017). Compressive sensing for direct time of flight estimation in ultrasound-based NDT. Paper presented at 11th International Workshop on Structural Health Monitoring, Stanford, United States.