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An approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification and
underwater buried target classification is presented in this paper. Work reported for sediment classification is
based on sonar data collected by one of the AN/AQS-20's sonars. Synthetic data, simulating data acquired by
parametric sonar, is employed for target classification. The technique is based on the Fractional Fourier Transform
(FrFT), which is better suited for sonar applications because FrFT uses linear chirps as basis functions. In the
first stage of the algorithm, FrFT requires finding the optimum order of the transform that can be estimated based
on the properties of the transmitted signal. Then, the magnitude of the Fractional Fourier transform for optimal
order applied to the backscattered signal is computed in order to approximate the magnitude of the bottom
impulse response. Joint time-frequency representations of the signal offer the possibility to determine the timefrequency
configuration of the signal as its characteristic features for classification purposes. The classification
is based on singular value decomposition of the time-frequency distributions applied to the impulse response.
A set of the largest singular values provides the discriminant features in a reduced dimensional space. Various
discriminant functions are employed and the performance of the classifiers is evaluated. Of particular interest
for underwater under-sediment classification applications are long targets such as cables of various diameters,
which need to be identified as different from other strong reflectors or point targets. Synthetic test data are
used to exemplify and evaluate the proposed technique for target classification. The synthetic data simulates
the impulse response of cylindrical targets buried in the seafloor sediments. Results are presented that illustrate
the processing procedure. An important characteristic of this method is that good classification accuracy of an
unknown target is achieved having only the response of a known target in the free field. The algorithm shows an
accurate way to classify buried objects under various scenarios, with high probability of correct classification.
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