Systems able to retrieve objects embedded in the seafloor are of crucial importance for many different tasks. An experimental assessment of a detector applying the "classify-before-detect" paradigm is proposed. The evaluation is based on real data acquired, during two sea trials, by two different sonar systems using low grazing angles and placed far from a target object. The "classify-before-detect" paradigm is a pattern-recognition approach to designing a classifier aimed at distinguishing between two classes (i.e., target presence and target absence), just like a detector. This approach has been selected and developed as it is very well suited to exploiting the available statistic and spectral a priori information on the target echo. In short, some features are extracted from the Wigner-Ville distribution and the bispectrum of partially overlapped short segments of the acquired echo signals. The dimensionality of the problem is reduced by the principal-component analysis, and the reduced feature vector is sent to a supervised statistical classifier. The ideal training set is composed of pure reverberation signals and the responses of the target in free field at different aspect angles.
Detection of objects buried in the seafloor by a pattern-recognition approach
IEEE Journal of Oceanic Engineering ; 26 , 4 ; 769-782
2001
14 Seiten, 40 Quellen
Article (Journal)
English
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