From the pioneering works of Gericke [O. R. Gericke, J. Acoust. Soc. Am. 35, 364–368 (1963)] and some other researchers [L. Adler, K. V. Cook, and W. A. Simpson, ‘‘Ultrasonic frequency analysis,’’ in Research Techniques in Nondestructive Testing, edited by R. S. Sharpe (Academic, London, 1977), Vol. 3; A. F. Brown, ‘‘Ultrasonic spectroscopy,’’ in Ultrasonic Testing, edited by J. Szilard (Wiley, New York, 1982)], frequency spectra of ultrasonic returns form hidden flaws carry a rich amount of information usable for flaw characterization. With a proper modeling of these ultrasonic echoes, the effectiveness of such frequency analyses can be further enhanced by a process called deconvolution or inverse filtering. In this paper, the performances of several deconvolution algorithms when applied to ultrasonic pulse echoes from artificial flaws embedded in some aluminum blocks are investigated. The relative computational complexities of these algorithms are also analyzed and compared. Empirical results shall justify the applications of these algorithms for flaw characterization. Furthermore, on comparing the experimental results, simpler implementations and higher efficiencies should favor the use of the spectrum-based deconvolution techniques over time-domain techniques.
Pattern recognition application to underwater acoustics is a relatively less explored area, even though much study has been made of sonar signal detection. Recently, significant effort has been made of submarine transient signal analysis and classification. Various spectral and time domain features are considered for detection and event classification. Effective recognition requires signal segmentation. The use of entropy distance measure for waveform segmentation is then examined. The next pattern recognition application is the target motion analysis by using pattern matching idea in the estimation of target range, velocity, and bearing. Another application is in multipath ranging. An image processing technique is used to extract the significant tracks from the correlograms to provide a continuous estimate of time delay or range under a multipath environment. Major computer results reported earlier [C. H. Chen, Pattern Recog. J. 16 (6) (1983)] along with further results on transient signal analysis are presented. Other applications such as sonar recognition in fisheries are also examined. While the trend continues to be digital processing and system integration, the basic recognition issue remains to be the extraction of effective features from the preprocessed underwater acoustical data. [Work partially supported by NUSC at Newport, RI.]
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