Source motion mitigation for adaptive matched field processing

Application of adaptive matched field processing to the problem of detecting quiet targets in shallow water is complicated by source motion, both the motion of the target and the motion of discrete interferers. Target motion causes spreading of the target peak, thereby reducing output signal power. Interferer motion increases the dimensionality of the interference subspace, reducing adaptive interference suppression. This paper presents three techniques that mitigate source motion problems in adaptive matched field processing. The first involves rank reduction, which enables adaptive weight computation over short observation intervals where motion effects are less pronounced. The other two techniques specifically compensate for source motion. Explicit target motion compensation reduces target motion mismatch by focusing snapshots according to a target velocity hypothesis. And time-varying interference filtering places time-varying nulls on moving interferers not otherwise suppressed by adaptive weights. The three techniques are applied to volumetric array data from the Santa Barbara Channel Experiment and are shown to improve output signal-to-background-plus-noise ratio by more than 3 dB over the standard minimum-variance, distortionless response adaptive beam-former. Application of the techniques in some cases proves to be the difference between detecting and not detecting the target

Environmental inversion using high-resolution matched-field processing

This paper considers the inversion of experimental field data collected with light receiving systems designed to meet operational requirements. Such operational requirements include system deployment in free drifting configurations and a limited number of acoustic receivers. A well-known consequence of a reduced spatial coverage is a poor sampling of the vertical structure of the acoustic field, leading to a severe ill-conditioning of the inverse problem and data to model cost function with a massive sidelobe structure having many local extrema. This causes difficulties to meta-heuristic global search methods, such as genetic algorithms, to converge to the true model parameters. In order to cope with this difficulty, broadband high-resolution processors are proposed for their ability to significantly attenuate sidelobes, as a contribution for improving convergence. A comparative study on simulated data shows that high-resolution methods did not outperform the conventional Bartlett processor for pinpointing the true environmental parameter when using exhaustive search. However, when a meta-heuristic technique is applied for exploring a large multidimensional search space, high-resolution methods clearly improved convergence, therefore reducing the inherent uncertainty on the final estimate. These findings are supported by the results obtained on experimental field data obtained during the Maritime Rapid Environmental Assessment 2003 sea trial. (c) 2007 Acoustical Society of America

Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

Improved bearing estimation in ocean by nonlinear wavelet denoising under non-Gaussian noise conditions

Bearing estimation of underwater acoustic sources is an important aspect of passive localization in the ocean. The performance of all bearing estimation techniques degrades under conditions of low signal-to-noise ratio (SNR) at the sensor array. The degradation may be arrested by denoising the array data before performing the task of bearing estimation. In the last few years, there has been considerable progress in the use of the wavelet transform for denoising signals. It is known that the traditional wavelet transform, which is a linear transformation, can be used for denoising signals in Gaussian noise; but this method is not suitable if the noise is strongly non-Gaussian. Statistical measurements of ocean acoustic ambient noise data indicate that the noise may have a significantly non-Gaussian heavy-tailed distribution in some environments. In this work, we have explored the possibility of employing nonlinear wavelet denoising [1, 2], a robust technique based on median interpolation, to improve the performance of bearing estimation techniques in ocean in a strongly non-Gaussian noise environment. We propose the application of nonlinear wavelet denoising to the noisy signal at each sensor in the array to boost the SNR before performing bearing estimation by known techniques such as MUSIC and Subspace Intersection Method [3]. Simulation results are presented to show that denoising leads to a significant reduction in the mean square errors (MSE) of the estimators, and enhancement of resolution of closely spaced sources

Improved localization of underwater acoustic sources by nonlinear wavelet denoising under non-Gaussian noise conditions

Bearing estimation of underwater acoustic sources is an important aspect of passive localization in the ocean. The performance of all bearing estimation techniques degrades under conditions of low signal-to-noise ratio (SNR) at the sensor array. The degradation may be arrested by denoising the array data before performing the task of bearing estimation. In the last few years, there has been considerable progress in the use of the wavelet transform for denoising signals. It is known that the traditionalwavelet transform, which is a linear transformation, can be used for denoising signals in Gaussian noise; but this method is not suitable if the noise is strongly non-Gaussian. Statistical measurements of ocean acoustic ambient noise data indicate that the noise may have a significantly non-Gaussian heavy-tailed distribution in some environments. In this work, we have explored the possibility of employing nonlinear wavelet denoising [1, 2], a robust technique based on median interpolation, to improve the performance of bearing estimation techniques in ocean in a strongly non-Gaussian noise environment. We propose the application of nonlinear wavelet denoising to the noisy signal at each sensor in the array to boost the SNR before performing bearing estimation by known techniques such as MUSIC and Subspace Intersection Method [3]. Simulation results are presented to show that denoising leads to a significant reduction in the mean square errors (MSE) of the estimators, and enhancement of resolution of closely spaced sources

Adjoint approach to the physical characterization of a shallow-water environment

In underwater acoustics a variety of different applications of adjoint models has been proposed in recent years. Adjoints have been derived for normal modes and for both the standard parabolic equation and Claerbout’s wide-angle approximation. This paper reviews the analytic nonlocal boundary control approach proposed in an earlier paper by the authors [Meyer & Hermand, ‘‘Optimal nonlocal boundary control of the wide-angle parabolic equation for inversion of a waveguide acoustic field,’’ J. Acoust. Soc. Am. 117, 2937–2948 (2005)] and presents a numerical extension that allows direct inversion of the geoacoustic parameters that are embedded in a discrete representation of the nonlocal boundary condition at the water-sediment interface. The effectiveness of this numerical adjoint approach for the physical characterization of a shallow-water environment is illustrated with applications for geoacoustic inversion and ocean acoustic tomography. In particular, it is shown how a joint inversion across multiple frequencies can enhance the performance of the optimization process, especially for the case of a sparse receiver array spanning part of the water column. In an additional example we combine the two applications and discuss the feasibility of geoacoustic inversion in the presence of an uncertain sound-speed profile

Fluid flow dynamics under location uncertainty

We present a derivation of a stochastic model of Navier Stokes equations that relies on a decomposition of the velocity fields into a differentiable drift component and a time uncorrelated uncertainty random term. This type of decomposition is reminiscent in spirit to the classical Reynolds decomposition. However, the random velocity fluctuations considered here are not differentiable with respect to time, and they must be handled through stochastic calculus. The dynamics associated with the differentiable drift component is derived from a stochastic version of the Reynolds transport theorem. It includes in its general form an uncertainty dependent "subgrid" bulk formula that cannot be immediately related to the usual Boussinesq eddy viscosity assumption constructed from thermal molecular agitation analogy. This formulation, emerging from uncertainties on the fluid parcels location, explains with another viewpoint some subgrid eddy diffusion models currently used in computational fluid dynamics or in geophysical sciences and paves the way for new large-scales flow modelling. We finally describe an applications of our formalism to the derivation of stochastic versions of the Shallow water equations or to the definition of reduced order dynamical systems

Source localization in shallow ocean using a vertical array of acoustic vector sensors

This paper introduces a new approach to 3D localisation of a narrowband acoustic source in a shallow ocean using acoustic vector sensors (AVS). Assuming a horizontally stratified and range-independent model of the ocean, it is shown that the azimuth of the source can be determined from the estimates of the horizontal components of the acoustic intensity vector obtained from the measurements of an AVS. The range and depth of the source could then be estimated through a 2D search to match the computed complex acoustic intensity vector expressed as a function of these parameters with its estimate obtained from the AVS measurements. However the search in range is computationally intensive as the range parameter is unbounded. We propose an alternative approach employing a vertical array of AVS, based on eigen-decomposition of the spatial correlation matrix of the data vector, leading to a closed form solution for the range parameter. The source depth is then estimated through a 1D search of this bounded parameter