Multiple target detection using split spectrum processing and groupdelay moving entropy

The split spectrum processing technique obtains a frequency-diverse ensemble of narrow-band signals through a filterbank then recombines them nonlinearly to improve target visibility. Although split spectrum processing is an effective method for suppressing grain noise in ultrasonic nondestructive testing, its application was mainly limited to the detection of single targets or multiple targets having similar spectral characteristics. In this paper, the group delay moving entropy technique is introduced primarily to enhance the performance of split spectrum processing in detecting multiple targets which exhibit different spectral characteristics (i.e., variations in target signal center frequency and bandwidth). This is likely to occur in complex, dispersive, and nonhomogeneous media such as composites, layered, and clad materials, etc. The analysis shows that the group delay moving entropy method can be used effectively to select the optimal frequency region for split spectrum processing when detecting such targets. Based on an iterative procedure that combines group delay moving entropy and split spectrum processing, multiple targets can be identified one at a time, and subsequently eliminated by using time domain windows. The removal of the dominant target improves the detection of the remaining weaker targets. Simulation results are presented which demonstrate the feasibility of the multistep split spectrum processing technique for detecting multiple targets in such materials