  The aim of this paper is to provide and numerically test in the presence of
measurement noise a procedure for target classification in wave imaging based
on comparing frequency-dependent distribution descriptors with precomputed ones
in a dictionary of learned distributions. Distribution descriptors for
inhomogeneous objects are obtained from the scattering coefficients. First, we
extract the scattering coefficients of the (inhomogeneous) target from the
perturbation of the echoes. Then, for a collection of inhomogeneous targets, we
build a frequency-dependent dictionary of distribution descriptors and use a
matching algorithm in order to identify a target from the dictionary up to some
translation, rotation and scaling.
