TY - JOUR
T1 - Adaptive methods for resolution enhancement of ultrasonic ndt signals
T2 - Comparative exploration 1
AU - Siddig, Mohammed
AU - Ki-Seong, Kim
N1 - Funding Information:
This study was supported by the Ministry of Knowledge Economy (MKE) through the Regional Inno vation Centre program.
PY - 2012/5
Y1 - 2012/5
N2 - Adaptive filters, with their efficiency and simplicity, have been used successfully in various ultrasonic NDT signal processing contexts. Of these, the adaptive deconvolution with the conven- tional least-mean-squares (LMS) adaptive filter has improved time resolution. However; the conver- gence speed of LMS is restricted by the eigenvalue spread of the input correlation matrix. This paper explores the potential of other adaptive algorithms, namely, normalized least-mean-squares (NLMS), recursive least squares (RLS) and QR-decomposition-based RLS (QR-RLS) to handle the deconvo- lution of ultrasonic NDT signals and compare their performances with that of the conventional LMS algorithm. Furthermore, the mean square error (MSE) behavior in the different adaptive filtering algorithms for ultrasonic NDT signals deconvolution is briefly introduced. Experiments results are explained by graphs and discussed based on the performance criteria. The proposed methods enhanced the resolution quality, offering more alternatives for this application according to specific case requirements.
AB - Adaptive filters, with their efficiency and simplicity, have been used successfully in various ultrasonic NDT signal processing contexts. Of these, the adaptive deconvolution with the conven- tional least-mean-squares (LMS) adaptive filter has improved time resolution. However; the conver- gence speed of LMS is restricted by the eigenvalue spread of the input correlation matrix. This paper explores the potential of other adaptive algorithms, namely, normalized least-mean-squares (NLMS), recursive least squares (RLS) and QR-decomposition-based RLS (QR-RLS) to handle the deconvo- lution of ultrasonic NDT signals and compare their performances with that of the conventional LMS algorithm. Furthermore, the mean square error (MSE) behavior in the different adaptive filtering algorithms for ultrasonic NDT signals deconvolution is briefly introduced. Experiments results are explained by graphs and discussed based on the performance criteria. The proposed methods enhanced the resolution quality, offering more alternatives for this application according to specific case requirements.
KW - Adaptive deconvolution
KW - Adaptive LMS
KW - Normalized LMS
KW - QR- decomposition-based RLS
KW - Recursive least-squares
UR - http://www.scopus.com/inward/record.url?scp=84869141649&partnerID=8YFLogxK
U2 - 10.1134/S1061830912050051
DO - 10.1134/S1061830912050051
M3 - Article
AN - SCOPUS:84869141649
SN - 1061-8309
VL - 48
SP - 285
EP - 290
JO - Russian Journal of Nondestructive Testing
JF - Russian Journal of Nondestructive Testing
IS - 5
ER -