Scattering resonances, filtering with reversible SAS processing, and applications of quantitative ray theory

Marston, Timothy M.; Marston, Philip L.; Williams, Kevin L.

doi:10.1109/oceans.2010.5664606

Cited by 18 publications

(12 citation statements)

References 20 publications

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“…The aluminum pipe was located in a proud configuration in the center of target field #4, and was oriented broadside to the rail. Other targets were located in the remaining four target fields, the results of which are discussed elsewhere [2,5]. Since multiple targets are deployed simultaneously, a "reversible SAS algorithm" is employed in order to isolate a single target response.…”

Section: Discussionmentioning

confidence: 99%

“…Since multiple targets are deployed simultaneously, a "reversible SAS algorithm" is employed in order to isolate a single target response. This method is described in detail in [5], and only the basic concept is given here. First, pulse compression and SAS image formation are performed using the method described previously.…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that an identical analysis is done for a proud aluminum cylinder (second target from the top in Fig. 4) and is the topic of [5]. The black boxes depict the spatial filter boundaries used to process the data.…”

Section: Discussionmentioning

confidence: 99%

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Measurements and modeling of the acoustic scattering from an aluminum pipe in the free field and in contact with a sand sediment

Espana

Williams

Kargl

et al. 2010

Oceans 2010 MTS/Ieee Seattle

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Recent experiments conducted in a fresh water pond investigated the monostatic scattering from an aluminum pipe (length-to-diameter ratio of 2) in the free field, as well as in a proud configuration on a flattened sand sediment. Synthetic aperture sonar (SAS) techniques are used to process the data. Absolute target strength is calculated over various spatial filter boundaries of the SAS images in order to isolate the specular and elastic responses of the pipe.A finite element (FE) model has been developed for the aluminum pipe in the free field, making use of the exact geometry associated with the pond experiment. The absolute target strength from these FE calculations is plotted in a similar manner to the experimental data, whereby the specular and elastic contributions are identified and compared to the data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Measurements and modeling of the acoustic scattering from an aluminum pipe in the free field and in contact with a sand sediment

Espana

Williams

Kargl

et al. 2010

Oceans 2010 MTS/Ieee Seattle

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“…Raw sonar data time series collected from the PondEX or TREX13 experiments are first pulse compressed with the transmit signal that was used in the data collection experiment and the result is further processed to remove returns from the neighboring objects to isolate the object of interest. This processing utilizes a reversible SAS imaging process, a spatial filtering process using a 2-D Tukey window, and a pseudo-inverse filtering [26]. This inverse filter maps the SAS image back to the pulse-compressed version that has less interface scattering …”

Section: Ac For Real Sonar Datamentioning

confidence: 99%

Underwater UXO classification using Matched Subspace Classifier with synthetic sparse dictionaries

Hall

Azimi-Sadjadi

Kargl

2016

OCEANS 2016 MTS/IEEE Monterey

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UNDERWATER UXO CLASSIFICATION USING MATCHED SUBSPACE CLASSIFIER WITH SYNTHETIC SPARSE DICTIONARIESClassification of underwater objects such as unexploded ordnances (UXO) and mines from sonar datasets poses a difficult problem. Among factors that complicate classification of these objects are: variations in the operating and environmental conditions, presence of spatially varying clutter, variations in target shape, composition, orientation and burial conditions. Furthermore, collection of large quantities of real and representative data for training and testing in various background conditions is very difficult and impractical in many cases. In order to remedy the lack of data availability, physical models of varying computational complexity are often used to supplement training databases with synthetically created samples which predict the response of known target models.In this thesis, we try to address several key questions for designing robust classifiers for UXO and munitions classification from low frequency sonar. These include: (1) "How can we form discriminative and highly separable features for describing UXO and non-UXO objects in a given dataset?", (2) "When do we reach a point of diminishing returns when utilizing synthetic models in a classifier's training?", and more importantly (3) "Which types of object variations cannot be modeled well by synthetic data?". Although, it may be somewhat ambitious to expect model data to capture all the essential features of proud or buried underwater objects for target characterization, these models can nevertheless provide us with clues on how to augment the training datasets to improve the robustness in different environmental conditions.ii Using empirically validated scattering models developed by University of Washington's Applied Physics Laboratory (APL-UW), fast ray models were acquired to generate the required synthetic training dataset for various UXO and non-UXO objects. A comprehensive analysis is then carried out on the classification performance of two subspace matching classifiers, trained on the synthetic data generated from this physical model, and tested on three real underwater sonar datasets. Both single and multi-aspect classification were considered using a combination of linear subspace models. Our classification hypothesis is that the spectral content of sonar backscatter display unique signatures providing good discrimination between different classes of objects. To develop a robust target classification method that can be applied to discriminate munitions from non-hostile man-made objects and competing natural clutter, the Matched Subspace Classifier (MSC) framework was adopted in conjunction with multidimensional Acoustic Color (AC) feature data extracted from raw sonar returns.Classification results of the MSC system constructed using two different signal subspace learning methods, namely K-SVD and locality preserving (LP) K-SVD are presented and benchmarked against each other. Additionally, a non-linear version of MSC using the...

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“…To obtain information on the scattering properties of a contact, including resonances, the SAS image is transformed to the multi-aspect acoustic colour domain using the following approach, as described in [11]:…”

Section: 32mentioning

confidence: 99%

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

Vossen¹,

Hunter²,

Duijster³

et al. 2015

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Scattering resonances, filtering with reversible SAS processing, and applications of quantitative ray theory

Cited by 18 publications

References 20 publications

Measurements and modeling of the acoustic scattering from an aluminum pipe in the free field and in contact with a sand sediment

Measurements and modeling of the acoustic scattering from an aluminum pipe in the free field and in contact with a sand sediment

Underwater UXO classification using Matched Subspace Classifier with synthetic sparse dictionaries

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

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