Recognition of propagating vibrations and invariant features for classification

Abstract

The vibrations produced by objects, for example by a plate or cylinder insonified by a sonar wave, exhibit characteristics unique to the particular structure, which can be used to distinguish among different objects. The situation is complicated, however, by many factors, a particularly important one being propagation through media. As a vibration propagates, its characteristics can change simply due to the propagation channel; for example, in a dispersive channel, the duration of the vibration will increase with propagation distance. These channel effects are clearly detrimental to automatic recognition because they do not represent the object of interest and they increase the variability of the measured responses, especially if measurements are obtained from targets at different locations. Our principal aim is to identify characteristics of propagating vibrations and waves that may be used as features for classification. We discuss various moment-like features of a propagating vibration. In the first set of moments, namely temporal moments such as mean and duration at a given location, we give explicit formulations that quantify the effects of dispersion. Accordingly, one can then compensate for the effects of dispersion on these moments. We then consider another new class of moments, which are invariant to dispersion and hence may be useful as features for dispersive propagation. We present classification results comparing these invariant features to related non-invariant features, for classification of simulated backscatter from different steel shells in a dispersive environment.