Dimitri Alexandrou

A methodology for acoustic seafloor classification

A seafloor classification methodology, based on a parameterization of the reverberation probability density function in conjunction with neural network classifiers, is evaluated through computer simulations. Different seafloor provides are represented by a number of scatterer distributions exhibiting various degrees of departure from the nominal Poisson distribution. Using a computer simulation program, these distributions were insonified at different spatial scales by varying the transmitted pulse length. The statistical signature obtained consists of reverberation kurtosis estimates as a function of pulse length. Two neural network classifiers are presented with the task of discriminating among the various scatterer distributions based on obtained acoustic signatures. The results indicate that this approach offers considerable promise for practical, realizable solutions to the problem of remote seafloor classification. >

Acoustic classification of abyssopelagic animals

The unique environment of the abyssal plains allows many simplifying assumptions, facilitating the acoustic classification of an animal into one of two groups. The most important assumptions are based on low population densities and available target strength histograms and swim rate histograms. The likelihood ratio is formed from this information and accepted signal processing theory. The likelihood function, a three-dimensional integral, is analytically simplified to one dimension and then solved numerically. A simulation based on this solution and measured data demonstrates that classification using the likelihood ratio approach is accurate, e.g. the sensitivity is >or=0.8. Although the measured data come from two abyssopelagic genera, the methods presented are more generally applicable. Simulations based on hypothetical animal populations show that under certain conditions, a near perfect classification can be made, e.g. sensitivity and specificity greater than 0.969. >

Application of a maximum likelihood processor to acoustic backscatter for the estimation of seafloor roughness parameters

Maximum likelihood (ML) estimation is used to extract seafloor roughness parameters from records of acoustic backscatter. The method relies on the Helmholtz–Kirchhoff approximation under the assumption of a power‐law roughness spectrum and on the statistical modeling of bottom reverberation. The result is a globally optimum, highly automated technique that is a useful tool in the context of seafloor classification via remote acoustic sensing. The general geometry of the Sea Beam bathymetric system is incorporated into the design of the ML processor in order to make it applicable to real acoustic data collected by this system. The processor is initially tested on simulated backscatter data and is shown to be very effective in estimating the seafloor parameters of interest. The simulated data are also used to study the effect of data averaging and normalization in the absence of system calibration information. The same estimation procedure is applied to real data collected over two central North Pacific sea...

High-resolution bathymetric simulations based on Kirchhoff scattering theory and anisotropic seafloor modeling

The bathymetric resolution of the seafloor map generated from multibeam or sidescan echo-sounder systems can be improved with various signal processing methods. In order to evaluate the accuracy and resolution performance of such techniques, the authors developed a realistic sonar simulation and seafloor modeling environment by merging the Kirchhoff scattering theory with the anisotropic seafloor parameterization of Goff and Jordan (1988). Multibeam sonar simulations utilizing the above environment, in combination with an eigenanalysis (e.g. MUSIC) power spectral estimation method for beamforming were carried out to obtain high-resolution bathymetry.<<ETX>>

Bayesian modeling of acoustic signals for seafloor identification

In this paper the Helmholtz–Kirchhoff approximation for backscattering strength and a Bayesian model of the uncertainty related to acoustic backscatter measurements are integrated into a maximum a posteriori processing scheme for the estimation of seafloor roughness parameters. Two processors are developed based on different levels of uncertainty regarding the angles of incidence for the received acoustic signals and the system calibration. Simulations indicate that the new maximum a posteriori processors are superior to a maximum likelihood estimation scheme that operates under the assumption that the angles of incidence for the backscattered signals are fixed and treats the calibration factor in a deterministic fashion. Specifically, the new processors produce roughness parameter estimates which are very close to the true values of the parameters, which are known in simulations, whereas the fixed angle processors are shown to result in a substantial bias in the estimation procedure.

Maximum a posteriori probability estimation of seafloor microroughness parameters from backscatter spatial coherence

A technique is presented for the estimation of a set of parameters associated with a geologically motivated model for seafloor microroughness due to Goff and Jordan (1988). The method seeks to connect the spatial covariance of the backscattered acoustic field with the correlation properties of the seafloor by constructing the a posteriori probability density function (pdf) of the parameters that define the seafloor microroughness wavenumber spectrum. The processor maximizes the joint a posteriori probability density of the model parameter set. Due to the complexity of the probability surface, the method of simulated annealing is used to search for the globally optimum solution vector.

Source localization based on coherent scattering from a rough seafloor in an uncertain underwater environment

In the present localization scenario, direct propagation from the source to the receiver is not permitted. The received field is comprised only by acoustic waves scattered from an infinitely hard seafloor and it is calculated using the Kirchhoff approximation. The performance of the optimum uncertain field processor is successfully tested in two mismatch situations, where either the location of the receiver array or the dimensions of bottom features are imperfectly known. The degradation of the localization performance due to discrepancies between the actual and the assumed scattering surface is demonstrated. This surface difference is expressed as a nondeterministic microroughness layer characterized by its root‐mean‐square height and its correlation length. Sensitivity study results are presented as a function of these two parameters. [Work supported by the Office of Naval Research, Code 1125GG, through contract No. ONR‐N00014‐93‐I‐0049 and Code 11250A, through contract No. ONR‐N00014‐91‐J‐1448.]

Bispectral analysis of ocean volume reverberation.

The univariate probability density function (pdf) of the volume reverberation process is typically modeled as symmetric and non‐Gaussian, with a variable kurtosis parameter. However, very little is known about the joint distribution of volume reverberation. In this study, the bispectral analysis of actual ocean volume reverberation data is performed in an effort to gain more insight into the structure of the joint probability density function of the volume reverberation process. The test vehicle is the Hinich test, a statistical method based on the sample bispectrum, which can be used to quantify the degree of non‐Gaussianity and asymmetry in the joint pdf of a random process. Simulated data records, distributed χ2 with decreasing numbers of degrees freedom, are first examined to provide a baseline measure of test statistic behavior as a function of pdf asymmetry. The test is then applied to actual volume reverberation data records obtained using sea beam. Although the empirically determined univariate de...

Seatrace: An Interactive Modeling And Visualization Package For Sound Propagation In The Ocean

The study of the propagation of sound in the ocean relies on ocean and sound propagation modeling. A new package, SeaTrace, has been developed to unite ocean modeling, computation of sound propagation, and interactive visualization in a single environment. SeaTrace allows the user to observe the propagation of signals in the ocean superimposed on graphical views of ocean characteristics. The distributed operation of the package is transparent to the user. The package makes extensive use of X Windows for the graphics. The code is portable and runs on many computational platforms. TM