Split-spectrum processing of broadband ultrasonic signals coupled with order statistic filtering has proven to be effective in improving the flaw-to-clutter ratio of backscattered signals. It is shown that an optimal rank can be obtained with a prior knowledge of flaw-to-clutter ratio and the underlying distributions. The order statistic filter performs well where the flaw and clutter echoes have good statistical separation in a given quantile region representing a particular rank (e.g. minimum, median, maximum). Order statistic filters are analyzed for the situation in which the observations do not contain equivalent statistical information. Experimental and simulated results are presented to show how effectively the order statistic filter can utilize information contained in different frequency bands to improve flaw detection.

Often the existence of reverberations in the ultrasonic pulse-echo imaging of multilayered structure prevents the direct characterization of each layer. The reverberating pattern is governed by the integrity of the layers, and deterioration of these layers can generate complex and unpredictable patterns. Analytical models for the classification of reverberant patterns using both normal and unique angle scanning schemes are presented. The theoretical model has been utilized in the experimental studies of a particular multilayered reverberant environment that exists in the detection of corrosion and/or volatile changes in stem generator tubing used in nuclear power plants. Various reverberant patterns can be recognized that are in close agreement with theoretical predictions.

In the pulse-echo method using broadband transducers, flaw detection can be improved by using optimal bandpass filtering to resolve flaw echoes surrounded by grain scatterers. Optimal bandpass filtering is achieved by examining spectral information of the flaw and grain echoes where frequency differences have been experimentally shown to be predictable in the Rayleigh scattering region. Using optimal frequency band information, flaw echoes can then be discriminated by applying adaptive thresholding techniques based on surrounding range cells. The authors present order-statistic (OS) processors, ranked and trimmed mean (TM), to robustly estimate the threshold while censoring outliers. The design of these OS processors is accomplished analytically based on constant false-alarm rate (CFAR) detection. It is shown that OS-CFAR and TM-CFAR processors can detect flaw echoes robustly with the CFAR of 10 (-4) where the range cell used for the threshold estimate contains outliers.

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Ultrasonic nondestructive imaging of critical defects (such as voids, microcracks, in bulk materials is often hampered by the presence of interfering and ran d om scatterers (i.e., clutter or grain echoes) associated with the defect's environment. The detection of broadband ultrasonic echoes for NDE can be improved by using optimal bandpass filtering to resolve flaw echoes surrounded by grain scatterers. The optimal bandpass filtering is achieved by examiniig spectral information of the flaw and the grain echoes where frequency differences have been experimentally shown to be predictable. The flaw echoes can then be dwriminated by using adaptive thresholding techniques based on surrounding range cells. This paper presents Order Statistic (OSL Based processors (ranked and trimmed mean) to ro ustly estimate the threshold while censoring outliers. The design of these OS processors are accomplished analytically based on Constant False Alarm Rate (CFAR) detection. The OS Based CFAR detectors were evaluated using experimental data and their performance is compared with the Cell Averaging (CA) method. It is shown that the OS Based CFAR processors can detect flaw echoes robustly where the range cell used for the threshold estimate contains outliers.