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In this paper, we consider the problem of denoising and classification of SONAR signals observed under compositional noise, i.e., they have been warped randomly along the -axis. The traditional techniques do not account for such noise and, consequently, cannot provide a robust classification of signals. We apply a recent framework that: 1) uses a distance-based objective function for data alignment...
We consider the problem of estimation and classification of signals in presence of compositional noise, where the traditional techniques do not provide either a consistent estimator for signals or a robust distance for classification. We use a recently introduced comprehensive framework that: (1) uses a distance-based objective function for data alignment leading to a consistent estimator of signals,...
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