  This paper introduces premier and innovative (real-time) multi-scale method
for target classification in electro-sensing. The intent is that of mimicking
the behavior of the weakly electric fish, which is able to retrieve much more
information about the target by approaching it. The method is based on a family
of transform-invariant shape descriptors computed from generalized polarization
tensors (GPTs) reconstructed at multiple scales. The evidence provided by the
different descriptors at each scale is fused using Dempster-Shafer Theory.
Numerical simulations show that the recognition algorithm we proposed performs
undoubtedly well and yields a robust classification.
