Neural network design has utilized flexible nonlinear processes which can mimic biological systems, but has suffered
from a lack of traceability in the resulting network. Graphical probabilistic models ground network design in
probabilistic reasoning, but the restrictions reduce the expressive capability of each node making network designs
complex. The ability to model coupled random variables using the calculus of nonextensive statistical mechanics
provides a neural node design incorporating nonlinear coupling between input states while maintaining the rigor of
probabilistic reasoning. A generalization of Bayes rule using the coupled product enables a single node to model
correlation between hundreds of random variables. A coupled Markov random field is designed for the inferencing and
classification of UCI’s MLR ‘Multiple Features Data Set’ such that thousands of linear correlation parameters can be
replaced with a single coupling parameter with just a (3%, 4%) reduction in (classification, inference) performance.
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.
In this paper we present a time-frequency approach for acoustic seabed classification. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), one of the five sonar systems in the AN/AQS-20. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm in order to determine the impulse response of the sediment. The Fractional Fourier transform requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Singular Value Decomposition and statistical properties of the Wigner and Choi-Williams distributions of the bottom impulse response are employed as features which are, in turn, used for classification. The Wigner distribution can be thought of as a signal energy distribution in joint time-frequency domain. Results of our study show that the proposed technique allows for accurate sediment classification of seafloor bottom data. Experimental results are shown and suggestions for future work are provided.
This paper presents several receiver structures for Chirp Slope Keying (CSK), a digital broadband modulation scheme we propose to use for underwater acoustical communications. In its simplest form, the binary information modulates the slope of a linear chirp, with up-chirps representing ones and down-chirps representing zeros. A time-domain receiver and a novel time-frequency receiver structure based on the Wigner distribution and the Radon Transform are discussed and evaluated in terms of the probability of error versus Signal-to-Noise (SNR) performance. Simulation results and plots are presented for the Additive White Gaussian Noise (AWGN) channel. Results show that if the detector at the receiver operates directly on the slope of the received signal, performance is improved at the expense of computational complexity.
KEYWORDS: Optical signal processing, Fourier transforms, Linear filtering, Signal processing, Deconvolution, Fractional fourier transform, Convolution, Radon transform, Commercial off the shelf technology, Time-frequency analysis
In this paper we present an approach for signal enhancement of sonar signals. Work reported is based on sonar data collected by the Volume Search Sonar (VSS), as well as VSS synthetic data. The Volume Search Sonar is a beamformed multibeam sonar system with 27 fore and 27 aft beams, covering almost the entire water volume (from above horizontal, through vertical, back to above horizontal). The processing of a data set of measurement in shallow water is performed using the Fractional Fourier Transform algorithm. The proposed technique will allow efficient determination of seafloor bottom characteristics and bottom type using the reverberation signal. A study is carried out to compare the performance of the presented method with conventional methods. Results are shown and future work and recommendations are presented.