NDLI: Experimental results of compressive sensing based imaging in ultrasonic non-destructive testing

Content Provider	IEEE Xplore Digital Library
Author	Azimipanah, A. ShahbazPanahi, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr., Software, & Comput. Eng., Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada (Azimipanah, A.; ShahbazPanahi, S.)
Abstract	In this paper, we use sparse signal recovery for non-destructive testing application, where the image of a test sample is extracted from ultrasonic array data. Using a frequency-domain model for the received signals, we propose two rearrangements of the data model to convert it to the format needed for sparse signal recovery. Each proposed approach is tested on the experimental data and the performance is compared with a MUSIC based imaging algorithm. The first rearrangement uses the measurement data obtained from individual transmitter elements in the array at a single frequency bin. We call this approach incoherent compressive sensing (IncCS). The second rearrangement is based on multiple measurement vectors (MMV) model. While the IncCS image has less background noise, the MMV results show better resolution in imaging the targets in the region of interest (ROI). The performance of the proposed approaches is better than MUSIC based algorithm. The MMV results also show that by using only half of the ultrasonic elements in the array, we can obtain an image which has comparable performance with the image obtained using the full array data.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	336
Ending Page	339
File Size	248380
Page Count	4
File Format	PDF
e-ISBN	9781467331463
DOI	10.1109/CAMSAP.2013.6714076
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-15
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transmitters Imaging Vectors Acoustics Multiple signal classification Arrays Compressed sensing
Content Type	Text
Resource Type	Article

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