Time-evolution modeling of seafloor scatter. II. Numerical and experimental evaluation

Bergem, O.; Pouliquen, E.; Canepa, Gaetano; Pace, Nicholas G.

doi:10.1121/1.424645

Cited by 24 publications

(14 citation statements)

References 3 publications

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“…It includes the probability density functions of the features through use of the covariance matrix K (9). It measures the distance between feature vectors in the cluster and the mean vector µ, for which the covariance has been measured, in units of standard deviations not in physical distance.…”

Section: B Mahalanobis Distancementioning

confidence: 99%

“…Classification of the seafloor is of considerable interest to researchers in underwater sonar [9], [10] and the development of theoretical models of echoes from rough surfaces continues to receive attention [11], [12]. Many underwater surfaces have a similar impedance to water so the problems are different to those in air where there is usually a large impedance difference across the air surface interface.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Surface Roughness With CTFM Ultrasonic Sensing

McKerrow

Kristiansen²

2006

IEEE Sensors J.

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Roughness is a characteristic of a surface that is a function of its geometry. Ultrasonic sensing in air provides range, area, and angle information because the surface geometry determines the characteristics of the echo. The authors introduce the "spatial-angle-filter model" to explain the impact of surface roughness on the echo. On the basis of this model, they design a set of features for use in classifying surfaces. The quality of the features and the classification is measured with the Mahalanobis distance. The resultant system is able to achieve 99.73% classification of a set of 12 surfaces using five features. Abstract-Roughness is a characteristic of a surface that is a function of its geometry. Ultrasonic sensing in air provides range, area, and angle information because the surface geometry determines the characteristics of the echo. The authors introduce the "spatial-angle-filter model" to explain the impact of surface roughness on the echo. On the basis of this model, they design a set of features for use in classifying surfaces. The quality of the features and the classification is measured with the Mahalanobis distance. The resultant system is able to achieve 99.73% classification of a set of 12 surfaces using five features. Disciplines Physical Sciences and MathematicsIndex Terms-Continuous transmission frequency modulated (CTFM) ultrasonic sensor, Mahalanobis classifier, spatial-angle filter, surface roughness, ultrasonic sensing, wheeled mobile robot.

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Section: B Mahalanobis Distancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Surface Roughness With CTFM Ultrasonic Sensing

McKerrow

Kristiansen²

2006

IEEE Sensors J.

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“…(8) or (9), and the largest set of values for D 1 and D 2 or D 0 1 and D 0 2 from either Eq. (11) or (12). The phase term in Eq.…”

Section: Predicted Scattering By a Pressure-release Plane Surfacementioning

confidence: 99%

Three-dimensional analysis of scattering by pressure-release plane surfaces and the validity of the image solution

Welton

2012

The Journal of the Acoustical Society of America

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Because of the complexity of the scattering integrals in three dimensions, numerous approximations are used to obtain closed-form solutions. By considering the scattering by an infinite, pressure-release plane surface, the effects of various phase approximations and source directivity approximations can be examined independently of the surface roughness. Calculations are carried out using the Fraunhofer and Fresnel phase approximations, and two directivity approximations. It has been shown experimentally that the image solution is valid for the reflection of an acoustic beam by an infinite, pressure-release plane surface if the plane is in the farfield of the source. Consequently, the image solution is used to compare analytical solutions obtained using various phase and directivity approximations, and it is found that both the Fresnel phase approximation and a realistic directivity approximation are required to achieve a good fit. The solution produced by the Fraunhofer phase approximation is obtained as an asymptotic limit of the modified Fresnel solution. Criteria for the validity of the Fraunhofer and Fresnel phase approximations are developed. The Fresnel phase approximation is valid under fairly broad conditions, but the Fraunhofer phase approximation is never valid for an infinite plane surface that must be in the farfield of the source.

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“…The algorithms are usually model-based by matching the signals acquired at sea with the output of models that mimic the measurement setup [1][2][3][4][5][6][7][8]. Our interest is restricted to the acoustic methods for seafloor characterization based on sonar systems.…”

Section: Introductionmentioning

confidence: 99%

Seafloor segmentation using angular backscatter responses obtained at sea with a forward-looking sonar system

et al. 2015

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a b s t r a c tSegmentation of the seabed is addressed by exploiting the local angular backscatter acoustic responses of the bottom. The study is based on data collected at sea with an experimental front-scan sonar system (COSMOS -Project partially funded by the European Commission in the MAST III program -Contract n°MAS3-CT97-0090 -DG12 -ESCY). Data were recorded during a three day survey in the Mediterranean Sea in high variability sediment areas. The front-looking geometry of the COSMOS system enables the collection of the local angular responses of the bottom over a wide range of incidence angles. The angular responses are projected on a principal components basis and subsequently classified by means of a density-based cluster identification method. The resulting segmentation maps delineate properly homogeneous zones of the seafloor that are in agreement with the ground-truth information provided by samples collected at sea.

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Time-evolution modeling of seafloor scatter. II. Numerical and experimental evaluation

Cited by 24 publications

References 3 publications

Classifying Surface Roughness With CTFM Ultrasonic Sensing

Classifying Surface Roughness With CTFM Ultrasonic Sensing

Three-dimensional analysis of scattering by pressure-release plane surfaces and the validity of the image solution

Seafloor segmentation using angular backscatter responses obtained at sea with a forward-looking sonar system

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