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Model-based estimation of thin multi-layered media using ultrasonic measurements
LKAB, Research & Development, 983 81 Malmberget.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-6216-6132
2009 (English)In: IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, ISSN 0885-3010, E-ISSN 1525-8955, Vol. 56, no 8, 1689-1702 p.Article in journal (Refereed) Published
Abstract [en]

In ultrasonic measurement situations, when dealing with media of multi-layered structures consisting of 1 or more thin layers, analysis of the measured ultrasonic waveform can be difficult because of overlapping and reverberant echoes. Information from the individual layers is then difficult to extract because the individual echoes cannot be detected. In this study, we use a parametric layer model to analyze the multi-layered material in a system identification approach. The parameters of the model are connected to physical properties of the investigated material, e.g., the reflection coefficients, the time-of-flight, and the attenuation. The main advantage using this model is that the complexity of the model is connected to the number of layers rather than the number of observable echoes in the received ultrasonic waveform. A system of linear equations is presented, giving the opportunity to find the model for both pulse-echo and through-transmission measurements. A thorough effort is made on the parameter estimation and optimization algorithm. The model is validated with practical measurements on a 3-layered structure using both pulse-echo and through-transmission techniques. The 3-layered material consists of a thin embedded middle layer with the time-of-flight in that layer shorter than the emitted signal¿s time support, giving rise to overlapping echoes. Finally the relation between the model parameters and physical properties of the material is established.

Place, publisher, year, edition, pages
2009. Vol. 56, no 8, 1689-1702 p.
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:ltu:diva-10196DOI: 10.1109/TUFFC.2009.1233Local ID: 8f4c67f0-f687-11dd-a85e-000ea68e967bOAI: oai:DiVA.org:ltu-10196DiVA: diva2:983136
Note
Validerad; 2009; 20090209 (frehag)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-24Bibliographically approved

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