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Dec 2008

Volume 124, Issue 6, pp. 3351-EL365

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Sonar signal processing using probabilistic signal and ocean environmental models

R. Lee Culver and H. John Camin

J. Acoust. Soc. Am. Volume 124, Issue 6, pp. 3619-3631 (2008); (13 pages) | Cited 2 times

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Acoustic signals propagating through the ocean are refracted, scattered, and attenuated by the ocean volume and boundaries. Many aspects of how the ocean affects acoustic propagation are understood, such that the characteristics of a received signal can often be predicted with some degree of certainty. However, acoustic ocean parameters vary with time and location in a manner that is not, and cannot be, precisely known; some uncertainty will always remain. For this reason, the characteristics of the received signal can never be precisely predicted and must be described in probabilistic terms. A signal processing structure recently developed relies on knowledge of the ocean environment to predict the statistical characteristics of the received signal, and incorporates this description into the processor in order to detect and classify targets. Acoustic measurements at 250 Hz from the 1996 Strait of Gibraltar Acoustic Monitoring Experiment are used to illustrate how the processor utilizes environmental data to classify source depth and to underscore the importance of environmental model fidelity and completeness.
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43.60.Cg Statistical properties of signals and noise
43.30.Re Signal coherence or fluctuation due to sound propagation/scattering in the ocean

Low probability of detection underwater acoustic communications using direct-sequence spread spectrum

T. C. Yang and Wen-Bin Yang

J. Acoust. Soc. Am. Volume 124, Issue 6, pp. 3632-3647 (2008); (16 pages) | Cited 4 times

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Direct-sequence spread spectrum is used for underwater acoustic communications between nodes, at least one of which is moving. At-sea data show that the phase change due to source motion is significant: The differential phase between two adjacent symbols is often larger than the phase difference between symbols. This poses a challenge to phase-detection based receiver algorithms when the source or receiver is moving. A pair of energy detectors that are insensitive to the phase fluctuations is proposed, whose outputs are used to determine the relationship between adjacent symbols. Good performance is achieved for a signal-to-noise ratio (SNR) as low as −10 dB based on at-sea data. While the method can be applied to signaling using short code sequences, the focus in this paper is on long code sequences for the purpose of achieving a high processing gain (at the expense of a low data rate), so that communications can be carried out at a low input SNR to minimize the probability of detection (PD) by an interceptor. PD is calculated for a typical shallow water environment as a function of range for several source levels assuming a broadband energy detector with a known signal bandwidth.
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43.60.Dh Signal processing for communications: telephony and telemetry, sound pickup and reproduction, multimedia

Moving microphone arrays to reduce spatial aliasing in the beamforming technique: Theoretical background and numerical investigation

Alfredo Cigada, Massimiliano Lurati, Francesco Ripamonti, and Marcello Vanali

J. Acoust. Soc. Am. Volume 124, Issue 6, pp. 3648-3658 (2008); (11 pages) | Cited 3 times

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This paper introduces a measurement technique aimed at reducing or possibly eliminating the spatial aliasing problem in the beamforming technique. Beamforming main disadvantages are a poor spatial resolution, at low frequency, and the spatial aliasing problem, at higher frequency, leading to the identification of false sources. The idea is to move the microphone array during the measurement operation. In this paper, the proposed approach is theoretically and numerically investigated by means of simple sound propagation models, proving its efficiency in reducing the spatial aliasing. A number of different array configurations are numerically investigated together with the most important parameters governing this measurement technique. A set of numerical results concerning the case of a planar rotating array is shown, together with a first experimental validation of the method.
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43.60.Fg Acoustic array systems and processing, beam-forming
43.60.Jn Source localization and parameter estimation
43.60.Lq Acoustic imaging, displays, pattern recognition, feature extraction
43.60.Qv Signal processing instrumentation, integrated systems, smart transducers, devices and architectures, displays and interfaces for acoustic systems

The time-reversal operator with virtual transducers: Application to far-field aberration correction

Jean-Luc Robert and Mathias Fink

J. Acoust. Soc. Am. Volume 124, Issue 6, pp. 3659-3668 (2008); (10 pages) | Cited 2 times

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The decomposition of the time-reversal operator (DORT) is a detection and focusing technique using an array of transmit receive transducers. It can extract Green’s functions of scatterers in a medium. A variant consists in transmitting focused beams (FDORT). It is shown here that the FDORT method can be interpreted as the decomposition of a time-reversal operator between an array of virtual transducers located at the transmit beams’ foci and the physical array. The receive singular vectors correspond to scatterers’ Green’s functions expressed in the physical array while the transmit singular vectors correspond to Green’s functions expressed in the virtual array. The position of the virtual array can be changed by varying the position of the foci, thus offering different points of view. Parameters and performance of some transmit schemes are discussed. Appropriately positioning the virtual transducers can simplify some problems. One application is measuring and correcting aberration in the case of a far-field phase screen model. Placing the virtual transducers near the phase screen transforms the problem in a simpler near-field phase screen problem.
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43.60.Fg Acoustic array systems and processing, beam-forming
43.80.Qf Medical diagnosis with acoustics
43.20.Fn Scattering of acoustic waves

Speech enhancement in discontinuous transmission systems using the constrained-stability least-mean-squares algorithm

J. M. Górriz, J. Ramírez, S. Cruces-Álvarez, D. Erdogmus, C. G. Puntonet, and E. W. Lang

J. Acoust. Soc. Am. Volume 124, Issue 6, pp. 3669-3683 (2008); (15 pages)

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In this paper a novel constrained-stability least-mean-squares (LMS) algorithm for filtering speech sounds is proposed in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the weight vector change under a stability constraint over the a posteriori estimation errors. To this purpose, the Lagrangian methodology has been used in order to propose a nonlinear adaptation in terms of the product of differential input and error. Convergence analysis is also studied in terms of the evolution of the natural modes to the optimal Wiener–Hopf solution so that the stability performance depends exclusively on the adaptation parameter μ and the eigenvalues of the difference matrix ΔR(1). The algorithm shows superior performance over the referenced algorithms in the ANC problem of speech discontinuous transmission systems, which are characterized by rapid transitions of the desired signal. The experimental analysis carried out on the AURORA 3 speech databases provides an extensive performance evaluation together with an exhaustive comparison to the standard LMS algorithms, i.e., the normalized LMS (NLMS), and other recently reported LMS algorithms such as the modified NLMS, the error nonlinearity LMS, or the normalized data nonlinearity LMS adaptation.
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43.60.Mn Adaptive processing
43.72.Lc Time and frequency alignment procedures for speech
43.72.Dv Speech-noise interaction

Fast volumetric integral-equation solver for high-contrast acoustics

E. Bleszynski, M. Bleszynski, and T. Jaroszewicz

J. Acoust. Soc. Am. Volume 124, Issue 6, pp. 3684-3693 (2008); (10 pages)

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An approach for solving volumetric integral equations in acoustics, applicable to problems involving large density contrasts, is described. While the conventional Lippmann–Schwinger integral equations become under such circumstances ill conditioned, the proposed approach reformulates them and casts them into an equivalent system of well-conditioned surface and volume integral equations. The corresponding fast solver [utilizing stiffness matrix compression based on fast Fourier transforms and characterized by O(N log N) solution complexity and storage requirements, where N is the number of unknowns] was enhanced to incorporate the proposed formulation. Features of the solution method and of the solver are illustrated on representative examples of numerically large problems.
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43.60.Pt Signal processing techniques for acoustic inverse problems
43.20.Bi Mathematical theory of wave propagation
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