Reverberation fluctuations from a smooth seafloor

Stanic, Steve; Kennedy, E.

doi:10.1109/48.219529

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…Such a model is typically referred to as a scale mixture of Gaussian distributions. In [10], it was shown that, for such a representation to exist, it is both necessary and sufficient that the pdf of , , be symmetric about the origin and have derivatives satisfying (1) Moreover, it was shown [10] that the distribution function of the random variable is very closely related to, and can be determined from, the Laplace transform of . One distribution that satisfies the requirement given in (1) is the Laplace distribution with pdf (2) Through the use of the Laplace transform of , one finds that is exponentially distributed with unity rate parameter [10].…”

Section: A Review and Density Characterizationmentioning

confidence: 98%

“…T HE studies on bottom return statistics reveal [1]- [3] that the distribution of the envelope of the matched filtered output is dependent on the frequency, grazing angle, range, and roughness properties of the bottom. Rough surface measurements made using high-resolution sonar have indicated [2] that the envelope amplitude distributions can often deviate from a Rayleigh distribution and be better modeled using log-normal, Weibull, or other more complex distributions.…”

Section: Introductionmentioning

confidence: 99%

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Non-Gaussian Target Detection in Sonar Imagery Using the Multivariate Laplace Distribution

Klausner

Azimi-Sadjadi

2015

IEEE J. Oceanic Eng.

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This paper introduces a new non-Gaussian detection method for complex-valued synthetic aperture sonar (SAS) imagery. The detection method is based on a multivariate extension of the Laplace distribution derived using a scale mixture of Gaussian distributions. A goodness-of-fit test in the form of a likelihood ratio is then conducted on a sonar imagery data set consisting of highfrequency (HF) and broadband (BB) images coregistered over the same region on the seafloor showing the proposed model's applicability in sonar imagery. Detection based on testing the equality of parameters from two populations is then implemented on a database containing actual SAS images of the seafloor with synthetically generated targets inserted into the images and compared to a similar non-Gaussian technique. Detection performance in this paper is given in terms of receiver-operator characteristic (ROC) curve attributes, probability of detection, and average false alarm rate.Index Terms-Binary hypothesis testing, multivariate Laplace, non-Gaussian detection, synthetic aperture sonar (SAS), underwater target detection.

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Section: A Review and Density Characterizationmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Non-Gaussian Target Detection in Sonar Imagery Using the Multivariate Laplace Distribution

Klausner

Azimi-Sadjadi

2015

IEEE J. Oceanic Eng.

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“…The background distribution in sonar data is highly variable. Studies on bottom return statistics reveal [36][37][38] that the distribution of the envelope of the matched filtered output is dependent on the frequency, grazing angle, range, and roughness properties of the sea-floor. Rough surface measurements made using high-resolution sonar have indicated that the amplitude distributions can often deviate from a Rayleigh distribution and be better modeled using log-normal, Weibull, or other more complex distributions [39].…”

Section: Introductionmentioning

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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“…Various probability density functions (pdfs) have been used to improve the performance of target detection, despeckling and identification of SAR images [33][34][35]. The scattering distribution of pdf parameter is highly sensitive to the frequency, range and roughness properties of the targets [36,37]. A small change in these properties provides a big change in the scattered distribution.…”

Section: Introductionmentioning

confidence: 99%

Development of an Adaptive Approach for Identification of Targets (Match Box, Pocket Diary and Cigarette Box) Under the Cloth With MMW Imaging System

Kumar¹,

Upadhyay²,

Singh³

2017

PIER B

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Abstract-Non-metallic objects, such as match box and cigarette box, detection and identification are quite an essential task during personal screening from standoff distance to protect the public places like the airport. Although various imaging sensors such as microwave, THz, infrared and MMW with signal processing techniques have been demonstrated by the researchers for concealed weapon detection, it is still a challenging task to detect and identify different types of small size targets such as matchbox, pocket diary and cigarette box simultaneously. Therefore, in this paper, an attempt has been made to develop such an algorithm/methodology by which different types of small targets, such as a matchbox and cigarette box, which is fully or half-filled or empty and pocket diary at different orientations beneath various cloths can be detected and identified with an MMW radar system. For this purpose, an optimal method has been proposed to form an image, and after that, in post processing a novel adaptive approach for detection and identification of considered targets has been proposed. The data were collected by MMW system at V-band (59 GHz-61 GHz). The proposed algorithm/methodology gives a quite satisfactory result.

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Reverberation fluctuations from a smooth seafloor

Cited by 22 publications

References 19 publications

Non-Gaussian Target Detection in Sonar Imagery Using the Multivariate Laplace Distribution

Non-Gaussian Target Detection in Sonar Imagery Using the Multivariate Laplace Distribution

Underwater UXO classification using Matched Subspace Classifier with synthetic sparse dictionaries

Development of an Adaptive Approach for Identification of Targets (Match Box, Pocket Diary and Cigarette Box) Under the Cloth With MMW Imaging System

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