SPECTRAL HISTOGRAM USING THE MINIMIZATION ALGORITHM THEORY AND APPLICATIONS TO FLAW DETECTION

Ultrasonic flaw detection in large grained materials is limited by the ability of the detection process to distinguish the flaw signals from the backscattered grain noise. This noise often masks the flaw signal, leading to difficulties in its detection and identification. In order to enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing was developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using the minimization algorithm, which selects the minimum magnitude of the normalized ensemble at each time instant, significant SNR enhancement can be obtained at the output. In this paper, the nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without a priori knowledge of the signal and noise spectra. The data from ultrasonic flaw detection experiments are also presented to support the analytical results.