Data-driven Localization and Structure Learning in Reverberant Underwater Acoustic Environments PDF Download
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Author: Toros Arikan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Passive localization and tracking of a mobile emitter, and the joint learning of its reverberant 3D environment, are important yet challenging tasks in the shallow-water underwater acoustic setting. A typical application is the monitoring of submarines or other man-made emitters with a small, surreptitiously-deployed receiver array. This task can be rendered more difficult by obstacles such as seamounts or piers, which can occlude the line of sight from the emitter to the receivers. Furthermore, the underwater acoustic domain is complex and difficult to model, and a good signal-to-noise ratio is not assured. We view these complexities as features that can be leveraged for improved localization performance, using global optimization and neural network methods. We develop a multi-stage optimization and tracking architecture that precisely maps the reflective boundaries in the environment, and thereby uses the non-line of sight reflected arrivals for robust and accurate localization. Each stage of this architecture establishes domain knowledge such as synchronization and occluder estimation, which are inputs for the following stages of more refined algorithms. Within this framework, we introduce a 2D neural network boundary estimation method that outperforms the existing methods in the literature, and is robust to the large time delay estimation errors that are common in the application domain. We analyze the performance and reliability of this holistic framework, both in simulation and in real-life reverberant watertank testbeds that model the shallow-water underwater acoustic setting. The results are encouraging for the future development of better-performing localization methods with novel capabilities, using data-driven learning algorithms.
Author: Toros Arikan
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Passive localization and tracking of a mobile emitter, and the joint learning of its reverberant 3D environment, are important yet challenging tasks in the shallow-water underwater acoustic setting. A typical application is the monitoring of submarines or other man-made emitters with a small, surreptitiously-deployed receiver array. This task can be rendered more difficult by obstacles such as seamounts or piers, which can occlude the line of sight from the emitter to the receivers. Furthermore, the underwater acoustic domain is complex and difficult to model, and a good signal-to-noise ratio is not assured. We view these complexities as features that can be leveraged for improved localization performance, using global optimization and neural network methods. We develop a multi-stage optimization and tracking architecture that precisely maps the reflective boundaries in the environment, and thereby uses the non-line of sight reflected arrivals for robust and accurate localization. Each stage of this architecture establishes domain knowledge such as synchronization and occluder estimation, which are inputs for the following stages of more refined algorithms. Within this framework, we introduce a 2D neural network boundary estimation method that outperforms the existing methods in the literature, and is robust to the large time delay estimation errors that are common in the application domain. We analyze the performance and reliability of this holistic framework, both in simulation and in real-life reverberant watertank testbeds that model the shallow-water underwater acoustic setting. The results are encouraging for the future development of better-performing localization methods with novel capabilities, using data-driven learning algorithms.
Author: Y. T. Chan
Publisher: Springer Science & Business Media
ISBN: 9400922892
Category : Science
Languages : en
Pages : 642
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Book Description
This book contains the papers that were accepted for presentation at the 1988 NATO Advanced Study Institute on Underwater Acoustic Data Processing, held at the Royal Military College of Canada from 18 to 29 July, 1988. Approximately 110 participants from various NATO countries were in attendance during this two week period. Their research interests range from underwater acoustics to signal processing and computer science; some are renowned scientists and some are recent Ph.D. graduates. The purpose of the ASI was to provide an authoritative summing up of the various research activities related to sonar technology. The exposition on each subject began with one or two tutorials prepared by invited lecturers, followed by research papers which provided indications of the state of development in that specific area. I have broadly classified the papers into three sections under the titles of I. Propagation and Noise, II. Signal Processing and III. Post Processing. The reader will find in Section I papers on low frequency acoustic sources and effects of the medium on underwater acoustic propagation. Problems such as coherence loss due to boundary interaction, wavefront distortion and multipath transmission were addressed. Besides the medium, corrupting noise sources also have a strong influence on the performance of a sonar system and several researchers described methods of modeling these sources.
Author: Lisheng Zhou
Publisher: Springer
ISBN: 9811024227
Category : Science
Languages : en
Pages : 126
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Book Description
These proceedings are a collection of 16 selected scientific papers and reviews by distinguished international experts that were presented at the 4th Pacific Rim Underwater Acoustics Conference (PRUAC), held in Hangzhou, China in October 2013. The topics discussed at the conference include internal wave observation and prediction; environmental uncertainty and coupling to sound propagation; environmental noise and ocean dynamics; dynamic modeling in acoustic fields; acoustic tomography and ocean parameter estimation; time reversal and matched field processing; underwater acoustic localization and communication as well as measurement instrumentations and platforms. These proceedings provide insights into the latest developments in underwater acoustics, promoting the exchange of ideas for the benefit of future research.
Author: Emma Ozanich
Publisher:
ISBN:
Category :
Languages : en
Pages : 171
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Book Description
Passive acoustics, or the recording of pressure signals from uncontrolled sound sources, is a powerful tool for monitoring man-made and natural sounds in the ocean. Passive acoustics can be used to detect changes in physical processes within the environment, study behavior and movement of marine animals, or observe presence and motion of ocean vessels and vehicles. Advances in ocean instrumentation and data storage have improved the availability and quality of ambient noise recordings, but there is an ongoing effort to improve signal processing algorithms for extracting useful information from the ambient noise. This dissertation uses machine learning as a framework to address problems in underwater passive acoustic signal processing. Statistical learning has been used for decades, but machine learning has recently gained popularity due to the exponential growth of data and its ability to capitalize on these data with efficient GPU computation. The chapters within this dissertation cover two types of problems: characterization and classification of ambient noise, and localization of passive acoustic sources. First, ambient noise in the eastern Arctic was studied from April to September 2013 using a vertical hydrophone array as it drifted from near the North Pole to north of Fram Strait. Median power spectral estimates and empirical probability density functions (PDFs) along the array transit show a change in the ambient noise levels corresponding to seismic survey airgun occurrence and received level at low frequencies and transient ice noises at high frequencies. Noise contributors were manually identified and included broadband and tonal ice noises, bowhead whale calling, seismic airgun surveys, and earthquake T phases. The bowhead whale or whales detected were believed to belong to the endangered Spitsbergen population and were recorded when the array was as far north as 86°24'N. Then, ambient noise recorded in a Hawaiian coral reef was analyzed for classification of whale song and fish calls. Using automatically detected acoustic events, two clustering processes were proposed: clustering handpicked acoustic metrics using unsupervised methods, and deep embedded clustering (DEC) to learn latent features and clusters from fixed-length power spectrograms. When compared on simulated signals of fish calls and whale song, the unsupervised clustering methods were confounded by overlap in the handpicked features while DEC identified clusters with fish calls, whale song, and events with simultaneous fish calls and whale song. Both clustering approaches were applied to recordings from directional autonomous seafloor acoustic recorder (DASAR) sensors on a Hawaiian coral reef in February 2020. Next, source localization in ocean acoustics was posed as a machine learning problem in which data-driven methods learned source ranges or direction-of-arrival directly from observed acoustic data. The pressure received by a vertical linear array was preprocessed by constructing a normalized sample covariance matrix (SCM) and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM) and random forests (RF). The FNN, SVM, RF and conventional matched-field processing were applied to recordings from ships in the Noise09 experiment to demonstrate the potential of machine learning for underwater source localization. The source localization problem was extended by examining the relationship between conventional beamforming and linear supervised learning. Then, a nonlinear deep feedforward neural network (FNN) was developed for direction-of-arrival (DOA) estimation for two-source DOA and for K-source DOA, where K is unknown. With multiple snapshots, K-source FNN achieved resolution and accuracy similar to Multiple Signal Classification (MUSIC) and SBL for an unknown number of sources. The practicality of the deep FNN model was demonstrated on ships in the Swellex96 experimental data.
Author:
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Category :
Languages : en
Pages :
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Abstract : Influenced by environmental conditions, underwater acoustic (UWA) communication channels exhibit spatial and temporal variations, posing significant challenges for UWA networking and applications. This dissertation develops statistical signal processing approaches to model and predict variations of the channel and relevant environmental factors. Firstly, extensive field experiments are conducted in the Great Lakes region. Three types of the freshwater river/lake acoustic channels are characterized in the aspects of statistical channel variations and sound propagation loss, including stationary, mobile and under-ice acoustic channels. Statistical data analysis shows that relative to oceanic channels, freshwater river/lake channels have larger temporal coherence, higher correlation among densely distributed channel paths, and less sound absorption loss. Moreover, variations of the under-ice channels are less severe than those in open water in terms of multipath structure and Doppler effect. Based on the observed channel characteristics, insights on acoustic transceiver design are provided, and the following two works are developed. online modeling and prediction of slowly-varying channel parameters are investigated, by exploiting their inherent temporal correlation and correlation with water environment. The temporal evolution of the channel statistics is modeled as the summation of a time-varying environmental process, and a Markov latent process representing unknown or unmeasurable physical mechanisms. An algorithm is developed to recursively estimate the unknown model parameters and predict the channel parameter of interest. The above model and the recursive algorithm are further extended to the channel that exhibits periodic dynamics. The proposed models and algorithms are evaluated via extensive simulations and data sets from two shallow-water experiments. The experimental results reveal that the average channel-gain-to-noise-power ratio, the fast fading statistics, and the average delay spread can be well predicted. The inhomogeneity of the sound speed distribution is challenging for Autonomous underwater vehicles (AUVs) communications and acoustic signaling-based AUV localization due to the refraction effect. Based on the time-of-flight (TOF) measurements among the AUVs, a distributed and cooperative algorithm is developed for joint sound speed estimation and AUV tracking. The joint probability distribution of the time-of-flight (TOF) measurements, the sound speed parameters and the AUV locations are represented by a factor graph, based on which a Gaussian message passing algorithm is proposed after the linearization of nonlinear measurement models. Simulation results show that the AUV locations and the sound speed parameters can be tracked with satisfying accuracy. Moreover, significant localization improvement can be achieved when the sound speed stratification effect is taken into consideration.
Author: Sonny Lin
Publisher:
ISBN: 9781632405081
Category : Underwater acoustics
Languages : en
Pages : 0
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Book Description
This book provides up-to-date information as well as introduction to underwater acoustics, which is described as the analysis of the propagation of sound in water and the interplay of the mechanical waves that constitute sound with the water and its boundaries. A wide range of topics are encompassed in this book like localization of buried objects in sediment with the help of high resolution array processing techniques, underwater acoustic source localization, adaptive strategy for underwater acoustic communication, etc. Researchers and scientists from across the world have contributed valuable data and information in this all-inclusive book. The aim of this elucidative book is to serve as a useful source of reference for readers including researchers, students and even scientists who are interested in acquiring knowledge regarding this field.
Author: Patrick Carroll
Publisher:
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Category :
Languages : en
Pages : 0
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 11
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The purpose of this project is to apply physics-based signal and array processing techniques, recently developed in the area of underwater acoustics, to atmospheric infrasound data and co-located seismic field data. The source of the infrasound data is the newly installed International Monitoring System (IMS) infrasound station at Pinon Flat (PFO). The seismic data are being collected by the Southern California ANZA seismic network. Installation of the eight sensors that comprise the infrasound station at PFO was completed by mid April of this year. The space filters of the array (18 m for the inner centered triangle elements and 70 m for the outer centered triangle elements) also are nearly all in place. Preliminary data collected by this array contain some signals with significant spatial coherence across the array aperture. In particular, a large event with high signal-to-noise ratio was recorded on 23 April. Analyses of the arrival structure of this signal are presented in this paper. In addition, the spatial and temporal properties of the background noise in relation to the local environmental conditions are discussed. A focused experiment involving the temporary installation of additional infrasound sensors to provide larger array aperture is being planned for this summer. A description of the planned experiment is presented below.
Author: Richard R. Fay
Publisher: Springer Science & Business Media
ISBN: 0387288635
Category : Science
Languages : en
Pages : 340
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Book Description
The Springer Handbook of Auditory Research presents a series of compreh- sive and synthetic reviews of the fundamental topics in modern auditory - search. The volumes are aimed at all individuals with interests in hearing research including advanced graduate students, postdoctoral researchers, and clinical investigators. The volumes are intended to introduce new investigators to important aspects of hearing science and to help established investigators to better understand the fundamental theories and data in ?elds of hearing that they may not normally follow closely. Each volume presents a particular topic comprehensively, and each serves as a synthetic overview and guide to the literature. As such, the chapters present neither exhaustive data reviews nor original research that has not yet appeared in peer-reviewed journals. The volumes focus on topics that have developed a solid data and conceptual foundation rather than on those for which a literature is only beginning to develop. New research areas will be covered on a timely basis in the series as they begin to mature.
Author: Chunshan Liu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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In this thesis, we develop and investigate novel signal processing techniques for underwater acoustic communication networks. Underwater acoustic channels differ from radio communication channels in the lower speed of signal propagation, richer and often sparse multipath arrivals, and more severe Doppler effect. Therefore, many signal processing techniques developed for radio communications may not work equivalently well for underwater acoustic channels. To investigate signal processing techniques in underwater acoustics, efficient simulation of signal transmission is required. Specifically, there is requirement for accurate simulation of doubly-selective underwater channels for different acoustic environments. In this thesis, a low-complexity channel simulator has been developed for scenarios with moving transmitter/receiver. The simulator is based on efficient generation of time-varying channel impulse response obtained using interpolation over a set of waymark impulse responses for a relatively small number of sampling points on the transmitter/receiver trajectory. The waymark impulse responses are generated using an acoustic field computation method, which is the most computationally expensive part of the simulator. To reduce the trajectory sampling rate, and thus, to reduce the complexity of the field computation, an approach for adjusting the time-varying multipath delays has been developed. For setting the trajectory sampling interval, a simple rule has been proposed, based on the waveguide invariant theory. To further reduce the simulator complexity, local spline interpolation is exploited. The developed simulator has been verified by comparing the simulated data with data from real ocean experiments. In particular, applying simulated data to an OFDM modem shows similar performance with that obtained from the data of a deep water experiment. In communication networks, knowledge of positions of communication nodes is important for improving the system performance. A multi-source localization technique has been proposed based on the matched field (MF) processing. The technique locates the nodes by solving a set of basis pursuit de-noising (BPDN) problems corresponding to a set of source frequencies. An efficient technique combining the homotopy approach and coordinate descent search has been developed to solve the BPDN problem. Further reduction in the complexity has been achieved by applying a position grid refinement method. Verified using simulated data generated by the proposed simulator and data from real experiment, the proposed technique outperforms other MF techniques in resolving sources positioned closely to each other, tolerance to noise and capability of locating multiple sources. To provide reliable localization based on MF techniques, accurate knowledge of the underwater acoustic environment is essential. However, such knowledge is not always available. Estimating uncertain environmental parameters can be achieved using MF inversion techniques. This requires solving a global optimization problem. Several global optimization algorithms have been investigated and an algorithm combining the simulated annealing and downhill simplex method has been applied for estimating the sound speed profile in a deep water scenario. Accurate MF localization results have been demonstrated when using the estimated sound speed profile. A very important task of communication receivers is accurate channel estimation. The knowledge of node positions and the environment can be exploited for enhancing the channel estimation accuracy and reducing the estimation complexity. This knowledge can be used to define the structure of the channel impulse response, such as the multipath spread and the sparsity. A channel estimator exploiting the channel sparsity estimated from the node positions has been proposed and investigated. The sparse taps of the channel impulse response are identified by solving a BPDN problem. The estimator employs an iterative tap-by-tap processing and uses local splines to interpolate the time-varying tap coefficients. This allows reduction in the complexity and memory requirement, whereas providing a high estimation accuracy.
