Classification of Cylindrical Targets Buried in Seafloor Sediments

Kaminsky, Edit J.; Barbu, Madalina

doi:10.1109/tpsd.2007.4380364

Cited by 2 publications

(2 citation statements)

References 13 publications

(14 reference statements)

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“…using simulated data and a discriminant analysis method applied to a time-frequency image [16]. Malarkodi et.…”

Section: Target Classificationmentioning

confidence: 99%

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Fischell¹

2015

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One of the long term goals of Autonomous Underwater Vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and expert image interpretation. This thesis proposes a vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target for lower cost-per-vehicle sensing and onboard, fully autonomous classification. The contributions of this thesis include the collection of novel high-quality bistatic data sets around spherical and cylindrical targets in situ during the BayEx'14 and Massachusetts Bay 2014 scattering experiments and the development of a machine learning methodology for classifying target shape and estimating orientation using bistatic amplitude data collected by an AUV. To achieve the high quality, densely sampled 3D bistatic scattering data required by this research, vehicle broadside sampling behaviors and an acoustic payload with precision timed data acquisition were developed. Classification was successfully demonstrated for spherical versus cylindrical targets using bistatic scattered field data collected by the AUV Unicorn as a part of the BayEx'14 scattering experiment and compared to simulated scattering models. The same machine learning methodology was applied to the estimation of orientation of aspect-dependent targets, and was demonstrated by training a model on data from simulation then successfully estimating the orientations of a steel pipe in the Massachusetts Bay 2014 experiment. The final models produced from real and simulated data sets were used for classification and parameter estimation of simulated targets in real time in the LAMSS MOOS-IvP simulation environment. fully autonomous underwater target characterization using AUVs. The demonstration of this technology with two field experiments was the highlight of her graduate work, and provided the data required for assessing the value of the methodology in the real world. Her current hobbies, used to maintain sanity during the process of completing this thesis, include hiking, cooking, gardening, music, reading, and thread crafts.

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“…using simulated data and a discriminant analysis method applied to a time-frequency image [16]. Malarkodi et.…”

Section: Target Classificationmentioning

confidence: 99%

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Fischell¹

2015

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“…Machine learning target classification, using acoustic data that include temporal or phase information from monostatic sensing, has been described by Kaminsky and Barbu (buried cylindrical targets such as cables) 12 and Malarkodi et al 13 (neural networks for classification of target type). A probabilistic method for monostatic target classification is discussed in Ref.…”

mentioning

confidence: 99%

Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields

Fischell

Schmidt

2015

The Journal of the Acoustical Society of America

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One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7–9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834–875 (2010)].

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Classification of Cylindrical Targets Buried in Seafloor Sediments

Cited by 2 publications

References 13 publications

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Characterization of underwater target geometry from Autonomous Underwater Vehicle sampling of bistatic acoustic scattered fields

Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields

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