Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks

The propagation of sound in a shallow water environment is characterized by boundary reflections from the sea surface and sea floor. These reflections result in multiple (indirect) sound propagation paths, which can degrade the performance of passive sound source localization methods. This paper proposes the use of convolutional neural networks (CNNs) for the localization of sources of broadband acoustic radiated noise (such as motor vessels) in shallow water multipath environments. It is shown that CNNs operating on cepstrogram and generalized cross-correlogram inputs are able to more reliably estimate the instantaneous range and bearing of transiting motor vessels when the source localization performance of conventional passive ranging methods is degraded. The ensuing improvement in source localization performance is demonstrated using real data collected during an at-sea experiment.Comment: 5 pages, 5 figures, Final draft of paper submitted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 15-20 April 2018 in Calgary, Alberta, Canada. arXiv admin note: text overlap with arXiv:1612.0350

Efficient use of space-time clustering for underwater acoustic communications

Underwater acoustic (UWA) communication channels are characterized by the spreading of received signals in space (direction of arrival) and in time (delay). The spread is often limited to a small number of space-time clusters. In this paper, the spacetime clustering is exploited in a proposed receiver designed for guard-free orthogonal frequency-division multiplexing (OFDM) with superimposed data and pilot signals. For separation of space clusters, the receiver utilizes a vertical linear array (VLA) of hydrophones, whereas for combining delay-spread signals within a space cluster, a time-domain equalizer is used. We compare a number of space-time processing techniques, including a proposed reduced-complexity spatial filter, and show that techniques exploiting the space-time clustering demonstrate an improved detection performance. The comparison is done using signals transmitted by a moving transducer, and recorded on a 14-element non-uniform VLA in sea trials at distances of 46 km and 105 km

Vector sensors for underwater : acoustic communications

Acoustic vector sensors measure acoustic pressure and directional components separately. A claimed advantage of vector sensors over pressure-only arrays is the directional information in a collocated device, making it an attractive option for size-restricted applications. The employment of vector sensors as a receiver for underwater communications is relatively new, where the inherent directionality, usually related to particle velocity, is used for signal-to-noise gain and intersymbol interference mitigation. The fundamental question is how to use vector sensor directional components to bene t communications, which this work seeks to answer and to which it contributes by performing: analysis of acoustic pressure and particle velocity components; comparison of vector sensor receiver structures exploring beamforming and diversity; quanti cation of adapted receiver structures in distinct acoustic scenarios and using di erent types of vector sensors. Analytic expressions are shown for pressure and particle velocity channels, revealing extreme cases of correlation between vector sensors' components. Based on the correlation hypothesis, receiver structures are tested with simulated and experimental data. In a rst approach, called vector sensor passive time-reversal, we take advantage of the channel diversity provided by the inherent directivity of vector sensors' components. In a second approach named vector sensor beam steering, pressure and particle velocity components are combined, resulting in a steered beam for a speci c direction. At last, a joint beam steering and passive time-reversal is proposed, adapted for vector sensors. Tested with two distinct experimental datasets, where vector sensors are either positioned on the bottom or tied to a vessel, a broad performance comparison shows the potential of each receiver structure. Analysis of results suggests that the beam steering structure is preferable for shorter source-receiver ranges, whereas the passive time-reversal is preferable for longer ranges. Results show that the joint beam steering and passive time-reversal is the best option to reduce communication error with robustness along the range.Sensores vetoriais acústicos (em inglês, acoustic vector sensors) são dispositivos que medem, alem da pressão acústica, a velocidade de partícula. Esta ultima, é uma medida que se refere a um eixo, portando, esta associada a uma direção. Ao combinar pressão acústica com componentes de velocidade de partícula pode-se estimar a direção de uma fonte sonora utilizando apenas um sensor vetorial. Na realidade, \um" sensor vetorial é composto de um sensor de pressão (hidrofone) e um ou mais sensores que medem componentes da velocidade de partícula. Como podemos notar, o aspecto inovador está na medição da velocidade de partícula, dado que os hidrofones já são conhecidos.(...)This PhD thesis was supported by the Brazilian Navy Postgraduate Study Abroad Program Port. 227/MB-14/08/2019

ACOUSTIC LOCALIZATION TECHNIQUES FOR APPLICATION IN NEAR-SHORE ARCTIC ENVIRONMENTS

The Arctic environment has undergone significant change in recent years. Multi-year ice is no longer prevalent in the Arctic. Instead, Arctic ice melts during summer months and re-freezes each winter. First-year ice, in comparison to multi-year ice, is different in terms of its acoustic properties. Therefore, acoustic propagation models of the Arctic may no longer be valid. The open water in the Arctic for longer time periods during the year invites anthropogenic traffic such as civilian tourism, industrial shipping, natural resource exploration, and military exercises. It is important to understand sound propagation in the first-year ice environment, especially in near-shore and shallow-water regions, where anthropogenic sources may be prevalent. It is also important to understand how to detect, identify, and track the anthropogenic sources in these environments in the absence of large acoustic sensory arrays. The goals of this dissertation are twofold: 1) Provide experimental transmission loss (TL) data for the Arctic environment as it now exists, that it may be used to validate new propagation models, and 2) Develop improved understanding of acoustic vector sensor (AVS) performance in real-world applications such as the first-year Arctic environment. Underwater and atmospheric acoustic TL have been measured in the Arctic environment. Ray tracing and parabolic equation simulations have been used for comparison to the TL data. Generally good agreement is observed between the experimental data and simulations, with some discrepancies. These discrepancies may be eliminated in the future with the development of improved models. Experiments have been conducted with underwater pa and atmospheric pp AVS to track mechanical noise sources in real-world environments with various frequency content and signal to noise ratio (SNR). A moving standard deviation (MSD) processing routine has been developed for use with AVS. The MSD processing routine is shown to be superior to direct integration or averaging of intensity spectra for direction of arrival (DOA) estimation. DOA error has been shown to be dependent on ground-reflected paths for pp AVS with analytical models. Underwater AVS have been shown to be feasible to track on-ice sources and atmospheric AVS have been shown feasible to track ground vehicle sources

압축센싱 기반 접근법을 이용한 수중음향 소음원의 위치 추정 기법 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 조선해양공학과, 2021. 2. 성우제.삼차원 음향 소음원의 위치추정은 잠수체, 산란체, 캐비테이션 소음원의 분석을 위해 필수적인 과정이다. 전통적인 빔형성 기법은 강인한 위치 추정 결과를 제공하나, 하나의 소음원의 위치만을 구분할 수 있는 저해상도의 결과를 보인다. 고해상도의 위치 추정 결과를 얻기 위해 최근 압축센싱 기반의 위치 추정 기법들이 사용되어 지고 있다. 압축센싱 기법은 희소성을 가진 신호의 획득,처리,복원에 효과적인 방법이며 영상처리, 수중음향, 최적화 문제 등에서 널리 활용되어지고 있다. 수중 소음원의 위치 추정을 위하여 압축센싱 기밥의 기법들이 적용되어 왔으며 전통적인 빔형성 기법에 비하여 해상도 측면에서 더 나은 성능을 보여주었다. 하지만 이러한 해상도 측면의 성능 향상에도 불구하고 압축센싱 기반의 방법은 여전히 문제점들을 가지고 있다. 첫번째, 압축센싱 기법은 전통적인 빔형성 기법에 비해 수치 연산 과정이 불안전성을 가진다. 비록 고해상도의 성능을 보여주나 압축센싱 기법은 수치해석 과정에서 불안정한 모습을 보여주며, 안정적인 복원을 저해한다. 두번째, 기저불일치로 인한 오차가 정확한 소음원의 위치 추정을 저해한다. 게다가 3차원 소음원의 위치 추정 문제는 이러한 기저 불일치를 해결할 수 있는 기법이 아직까지 개발되지 못하였다. 본 논문은 기존의 압축센싱 기반의 위치 추정 기법이 가지는 문제점을 파악하고 3차원 위치 추정 문제를 다룰 수 있는 향상된 압축센싱 기법을 소개한다. 탐색 공간 사이의 높은 상관관계로 인하여 발생하는 해의 불안정성을 해결아기 위하여 ``다중주파수 상관 처리기법"을 소개하고, 3차원 위치 추정문제에서 기저불일치 문제를 해결할 수 있는 ``유동 탐색 격자 기법"을 소개한다. 제안된 기법은 전통적인 빔형성 기법에 비하여 정확한 위치 추정 결과를 제공하며 실험 데이터를 통한 위치 추정결과를 이용하여 이러한 주장을 뒷받침하였다. 본 연구에서는 수중음향 소음원의 3차원 위치 추정 문제를 중점적으로 다루었으나, 제안된 기법은 소나 및 레이더, 음향 소음원 위치 추정 문제에도 효과적으로 적용될 수 있을 것으로 기대된다.Three-dimensional acoustic localization is an essential process to analyze the underwater sound sources such as submarine, scatterer, marine cavitation. Traditional beamforming processors provide robust localization results, however, the results show a low-resolution result which only reveals one dominant source location. In order to obtain the high resolution localization results, compressive sensing(CS) based approaches have been used recently. CS technique is an effective way for acquiring, processing, reconstructing the sparse signal and has wide applicability to many research fields such as image processing, underwater acoustics and optimization problems. For localizing the underwater acoustic sources, CS-based approaches have been adopted in many research fields and have shown better localization performance compared to the traditional beamforming processors in terms of resolution. Despite the performance improvement in resolution, there are still problems that need to be resolved when using the CS-based method. First, the CS-based method does not appear to be robust compared with the traditional beamforming processors. CS-based method provides high-resolution results, however, it suffers from computational instability which hinders the stable reconstruction. Second, basis mismatch error hindrances estimating the exact source locations. Moreover, there is no basis mismatch estimation technique applicable to 3D source localization problem. This dissertation points out the limitation of conventional CS-based localization method and introduces the advanced CS-based localization method which deals with 3D source localization problem. The ``coherent multiple-frequency processing" is introduced to overcome the instability of solution induced by high correlation of spatial grids and ``flexible searching-grid technique" is introduced to solve the basis mismatch problem which is developed for 3D source localization problem. The suggested techniques provide more accurate localization results compared to traditional beamforming processors or conventional CS-based beamforming processors and the arguments are backed with actual experimental data which was conducted in a cavitation tunnel. Though underwater acoustic source localization problems are presented in this dissertation, the proposed technique can be extended to many research fields, such as radar detection, sonar detection, ultrasound imaging.1 Introduction 2 1.1 Issue 1 : Computational Stability 4 1.2 Issue 2 : Basis Mismatch 5 1.3 Organization of the Dissertation 5 2 CS techniques for three-dimensional source localization 9 2.1 Compressive Sensing (CS) 9 2.2 Block-Sparse Compressive Sensing (BSCS) 11 2.3 Sparse Bayesian learning (SBL) 12 2.4 Off-Grid Sparse Bayesian Inference (OGSBI) 14 3 3D CS-based source localization method using multiple-frequency components 18 3.1 Introduction 18 3.2 Block-sparse Compressive Sensing for Incipient Tip Vortex Cavitation Localization 24 3.2.1 System framework for incipient tip vortex cavitation localization 24 3.2.2 Incoherent multiple-frequency localization with compressive sensing 26 3.2.3 Coherent multiple-frequency localization with block-sparse compressive sensing 28 3.3 Localization Results for Incipient TVC 32 3.3.1 Transducer source experiment 33 3.3.2 Incipient TVC Noise Source Experiment 36 3.4 Conclusion 41 3.5 Acknowledgments 43 4 3D CS-based source localization method by reducing the basis mismatch error 48 4.1 Introduction 48 4.2 Off grid system framework for 3D source localization 50 4.2.1 System framework for 3-dimensional off gird source localization 50 4.2.2 Coherent multiple-frequency localization with block-sparse Bayesian learning technique 53 4.2.3 3-dimensional off grid source localization method 55 4.3 Simulation and Experiment Results 62 4.4 Conclusion 65 5 Summary 70 Abstract (In Korean) 73Docto

Ocean parameter estimation with high-frequency signals using a vector sensor array

Tese de dout., Engenharia Electrónica e Telecomunicações (Processamento de Sinal), Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2012Vector sensors began to emerge in 1980s as potential competitors to omni directional pressure driven hydrophones, while their practical usage in underwater applications started in the last two decades. The crucial advantage of vector sensors relative to hydrophones is that they are able to record both the omni-directional pressure and the three vectorial components of the particle velocity. A claimed advantage of vector sensors over hydrophones is the quantity of information obtained from a single point spatial device, which potentially allows for high performance small aperture Vector Sensor Arrays (VSA). The capabilities of such small aperture VSA have captured the attention for their usage in high-frequency applications. The main contribution of this work is the understanding of the gain provided by vector sensors over hydrophones whenever ocean environmental parameter estimation is concerned. In a rst step a particle velocity-pressure joint data model is proposed and an extended VSA-based Bartlett estimator is derived. This data model and estimator, initially developed for estimating direction of arrival, are generalized for ocean parameter estimation, assuming a particle velocity capable physical model - the TRACEO model. The highlighted capabilities of the VSA are rst demonstrated for angle of arrival estimation, where a variety of spatial con gurations of hydrophone arrays are compared to that of a vertical VSA. A vertical VSA array con guration is then used for estimating geoacoustic bottom properties from short range acoustic data, using two VSA-based techniques: the generalized Bartlett estimator and the re ection coe cient estimator proposed by Harrison et al.. The proposed techniques where tested on experimental VSA data recorded in shallow water area o the Island of Kauai (Hawaii) during the MakaiEx 2005 experiment. The obtained results are comparable between techniques and inline with the expected values for that region. These results suggest that it is indeed possible to obtain reliable seabed geoacoustic properties' estimates in a frequency band of 8-14 kHz using a small aperture VSA with only a few sensors.Fundação para a Ciência e a Tecnologia (FCT

Sensor Array Processing with Manifold Uncertainty

<p>The spatial spectrum, also known as a field directionality map, is a description of the spatial distribution of energy in a wavefield. By sampling the wavefield at discrete locations in space, an estimate of the spatial spectrum can be derived using basic wave propagation models. The observable data space corresponding to physically realizable source locations for a given array configuration is referred to as the array manifold. In this thesis, array manifold ambiguities for linear arrays of omni-directional sensors in non-dispersive fields are considered. </p><p>First, the problem of underwater a hydrophone array towed behind a maneuvering platform is considered. The array consists of many hydrophones mounted to a flexible cable that is pulled behind a ship. The towed cable will bend or distort as the ship performs maneuvers. The motion of the cable through the turn can be used to resolve ambiguities that are inherent to nominally linear arrays. The first significant contribution is a method to estimate the spatial spectrum using a time-varying array shape in a dynamic field and broadband temporal data. Knowledge of the temporal spectral shape is shown to enhance detection performance. The field is approximated as a sum of uncorrelated planewaves located at uniform locations in angle, forming a gridded map on which a maximum likelihood estimate for broadband source power is derived. Uniform linear arrays also suffer from spatial aliasing when the inter-element spacing exceeds a half-wavelength. Broadband temporal knowledge is shown to significantly reduce aliasing and thus, in simulation, enhance target detection in interference dominated environments. </p><p>As an extension, the problem of towed array shape estimation is considered when the number and location of sources are unknown. A maximum likelihood estimate of the array shape using the field directionality map is derived. An acoustic-based array shape estimate that exploits the full 360$^\circ$ field via field directionality mapping is the second significant contribution. Towed hydrophone arrays have heading sensors in order to estimate array shape, but these sensors can malfunction during sharp turns. An array shape model is described that allows the heading sensor data to be statistically fused with heading sensor. The third significant contribution is method to exploit dynamical motion models for sharp turns for a robust array shape estimate that combines acoustic and heading data. The proposed array shape model works well for both acoustic and heading data and is valid for arbitrary continuous array shapes.</p><p>Finally, the problem of array manifold ambiguities for static under-sampled linear arrays is considered. Under-sampled arrays are non-uniformly sampled with average spacing greater than a half-wavelength. While spatial aliasing only occurs in uniformly sampled arrays with spacing greater than a half-wavelength, under-sampled arrays have increased spatial resolution at the cost of high sidelobes compared to half-wavelength sampled arrays with the same number of sensors. Additionally, non-uniformly sampled arrays suffer from rank deficient array manifolds that cause traditional subspace based techniques to fail. A class of fully agumentable arrays, minimally redundant linear arrays, is considered where the received data statistics of a uniformly spaced array of the same length can be reconstructed in wide sense stationary fields at the cost of increased variance. The forth significant contribution is a reduced rank processing method for fully augmentable arrays to reduce the variance from augmentation with limited snapshots. Array gain for reduced rank adaptive processing with diagonal loading for snapshot deficient scenarios is analytically derived using asymptotic results from random matrix theory for a set ratio of sensors to snapshots. Additionally, the problem of near-field sources is considered and a method to reduce the variance from augmentation is proposed. In simulation, these methods result in significant average and median array gains with limited snapshots.</p>Dissertatio