High-resolution spatial processing in underwater acoustics /

Mode of access: Internet.

Underwater Target Localization, Tracking and Classification

Underwater target localization and tracking attracts tremendous research interest due to various impediments to the estimation task caused by the noisy ocean environment. This thesis envisages the implementation of a prototype automated system for underwater target localization, tracking and classification using passive listening buoy systems and target identification techniques. An autonomous three buoy system has been developed and field trials have been conducted successfully. Inaccuracies in the localization results, due to changes in the environmental parameters, measurement errors and theoretical approximations are refined using the Kalman filter approach. Simulation studies have been conducted for the tracking of targets with different scenarios even under maneuvering situations. This system can as well be used for classifying the unknown targets by extracting the features of the noise emanations from the targets.

Nonlinear acoustics, 1954 to 1983.

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Hydroaccoustic pinging as a diver tool for underwater navigation in surveying Puget Sound Eelgrass (Zostera marina) densities

Senior thesis written for Oceanography 445

The ambient soundscape of inland waters in Seattle, Washington: bridge traffic as a source of urban underwater noise pollution?

Senior thesis written for Oceanography 445

Probabilistic algorithms for blind adaptive multiuser detection

In this paper, two probabilistic adaptive algorithmsfor jointly detecting active users in a DS-CDMA system arereported. The first one, which is based on the theory of hiddenMarkov models (HMM’s) and the Baum–Wech (BW) algorithm,is proposed within the CDMA scenario and compared withthe second one, which is a previously developed Viterbi-basedalgorithm. Both techniques are completely blind in the sense thatno knowledge of the signatures, channel state information, ortraining sequences is required for any user. Once convergencehas been achieved, an estimate of the signature of each userconvolved with its physical channel response (CR) and estimateddata sequences are provided. This CR estimate can be used toswitch to any decision-directed (DD) adaptation scheme. Performanceof the algorithms is verified via simulations as well as onexperimental data obtained in an underwater acoustics (UWA)environment. In both cases, performance is found to be highlysatisfactory, showing the near–far resistance of the analyzed algorithms.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Submerged macrophytes height estimation by echosounder data sample

Traditional methods of submerged aquatic vegetation (SAV) survey last long and then, they are high cost. Optical remote sensing is an alternative, but it has some limitations in the aquatic environment. The use of echosounder techniques is efficient to detect submerged targets. Therefore, the aim of this study is to evaluate different kinds of interpolation approach applied on SAV sample data collected by echosounder. This study case was performed in a region of Uberaba River - Brazil. The interpolation methods evaluated in this work follow: Nearest Neighbor, Weighted Average, Triangular Irregular Network (TIN) and ordinary kriging. Better results were carried out with kriging interpolation. Thus, it is recommend the use of geostatistics for spatial inference of SAV from sample data surveyed with echosounder techniques. © 2012 IEEE.

Sistema para monitoramento e análise de paisagens acústicas submarinas.

O Monitoramento Acústico Passivo (PAM) submarino refere-se ao uso de sistemas de escuta e gravação subaquática, com o intuito de detectar, monitorar e identificar fontes sonoras através das ondas de pressão que elas produzem. Se diz que é passivo já que tais sistemas unicamente ouvem, sem perturbam o meio ambiente acústico existente, diferentemente de ativos, como os sonares. O PAM submarino tem diversas áreas de aplicação, como em sistemas de vigilância militar, seguridade portuária, monitoramento ambiental, desenvolvimento de índices de densidade populacional de espécies, identificação de espécies, etc. Tecnologia nacional nesta área é praticamente inexistente apesar da sua importância. Neste contexto, o presente trabalho visa contribuir com o desenvolvimento de tecnologia nacional no tema através da concepção, construção e operação de equipamento autônomo de PAM e de métodos de processamento de sinais para detecção automatizada de eventos acústicos submarinos. Foi desenvolvido um equipamento, nomeado OceanPod, que possui características como baixo custo de fabrica¸c~ao, flexibilidade e facilidade de configuração e uso, voltado para a pesquisa científica, industrial e para controle ambiental. Vários protótipos desse equipamento foram construídos e utilizados em missões no mar. Essas jornadas de monitoramento permitiram iniciar a criação de um banco de dados acústico, o qual permitiu fornecer a matéria prima para o teste de detectores de eventos acústicos automatizados e em tempo real. Adicionalmente também é proposto um novo método de detecção-identificação de eventos acústicos, baseado em análise estatística da representação tempo-frequência dos sinais acústicos. Este novo método foi testado na detecção de cetáceos, presentes no banco de dados gerado pelas missões de monitoramento.

The stability of air bubbles in the sea and the effect of bubbles and particles in the extinction of sound and light in sea water /

Mode of access: Internet.

Acoustic radiation of a cylindrical bar and an infinite plate excited by the field of a ship propeller,

Photocopy.

Pulsed parabolic equation model of acoustic transmission (PPEMAT)

Underwater sound is very important in the field of oceanography where it is used for remote sensing in much the same way that radar is used in atmospheric studies. One way to mathematically model sound propagation in the ocean is by using the parabolic-equation method, a technique that allows range dependent environmental parameters. More importantly, this method can model sound transmission where the source emits either a pure tone or a short pulse of sound. Based on the parabolic approximation method and using the split-step Fourier algorithm, a computer model for underwater sound propagation was designed and implemented. This computer model differs from previous models in its use of the interactive mode, structured programming, modular design, and state-of-the-art graphics displays. In addition, the model maximizes the efficiency of computer time through synchronization of loosely coupled dual processors and the design of a restart capability. Since the model is designed for adaptability and for users with limited computer skills, it is anticipated that it will have many applications in the scientific community.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Passive Acoustics: A Multifaceted Tool for Marine Mammal Conservation

Marine mammals exploit the efficiency of sound propagation in the marine environment for essential activities like communication and navigation. For this reason, passive acoustics has particularly high potential for marine mammal studies, especially those aimed at population management and conservation. Despite the rapid realization of this potential through a growing number of studies, much crucial information remains unknown or poorly understood. This research attempts to address two key knowledge gaps, using the well-studied bottlenose dolphin (Tursiops truncatus) as a model species, and underwater acoustic recordings collected on four fixed autonomous sensors deployed at multiple locations in Sarasota Bay, Florida, between September 2012 and August 2013. Underwater noise can hinder dolphin communication. The ability of these animals to overcome this obstacle was examined using recorded noise and dolphin whistles. I found that bottlenose dolphins are able to compensate for increased noise in their environment using a wide range of strategies employed in a singular fashion or in various combinations, depending on the frequency content of the noise, noise source, and time of day. These strategies include modifying whistle frequency characteristics, increasing whistle duration, and increasing whistle redundancy. Recordings were also used to evaluate the performance of six recently developed passive acoustic abundance estimation methods, by comparing their results to the true abundance of animals, obtained via a census conducted within the same area and time period. The methods employed were broadly divided into two categories – those involving direct counts of animals, and those involving counts of cues (signature whistles). The animal-based methods were traditional capture-recapture, spatially explicit capture-recapture (SECR), and an approach that blends the “snapshot” method and mark-recapture distance sampling, referred to here as (SMRDS). The cue-based methods were conventional distance sampling (CDS), an acoustic modeling approach involving the use of the passive sonar equation, and SECR. In the latter approach, detection probability was modelled as a function of sound transmission loss, rather than the Euclidean distance typically used. Of these methods, while SMRDS produced the most accurate estimate, SECR demonstrated the greatest potential for broad applicability to other species and locations, with minimal to no auxiliary data, such as distance from sound source to detector(s), which is often difficult to obtain. This was especially true when this method was compared to traditional capture-recapture results, which greatly underestimated abundance, despite attempts to account for major unmodelled heterogeneity. Furthermore, the incorporation of non-Euclidean distance significantly improved model accuracy. The acoustic modelling approach performed similarly to CDS, but both methods also strongly underestimated abundance. In particular, CDS proved to be inefficient. This approach requires at least 3 sensors for localization at a single point. It was also difficult to obtain accurate distances, and the sample size was greatly reduced by the failure to detect some whistles on all three recorders. As a result, this approach is not recommended for marine mammal abundance estimation when few recorders are available, or in high sound attenuation environments with relatively low sample sizes. It is hoped that these results lead to more informed management decisions, and therefore, more effective species conservation.