A wavefront adaptive sensing beamformer for ocean acoustic waveguides.

This paper addresses robust adaptive beamforming for passive sonar in uncertain, shallow-water environments. Conventional beamforming is still common in passive sonar because adaptive beamformers suffer from signal mismatch in complex multipath environments. Existing approaches to robust adaptive beamforming try to model and account for the uncertainty in the beamformer's hypothesized signal subspace by using additional linear or quadratic constraints. Doing so, however, reduces the adaptivity of the beamformer and is prone to insufficiently suppressing interference. Instead, this paper uses blind source separation methods to adaptively estimate the complex spatial wavefronts of both targets and interference without requiring detailed physical modeling of the channel. By exploiting the different temporal spectra and/or frequency-selective multipath fading of targets and interference, this approach constructs a "signal-free" covariance matrix without imposing directional gain constraints. In doing so, the wavefront adaptive sensing (WAS) beamformer is able to separate targets from strong interference that is within the conventional beam width of the target. Simulation results in a realistic shallow-water channel are presented as well as results using the SWellEx96 S59 data with an injected target to show that the proposed WAS beamformer outperforms conventional and minimum variance adaptive beamformers in a shallow-water scenario.

Maximum-likelihood spatial spectrum estimation in dynamic environments with a short maneuverable array.

This work concerns the development of field directionality mapping algorithms for short acoustic arrays on mobile maneuverable platforms that avoid the left/right ambiguities and endfire resolution degradation common to longer non-maneuverable line arrays. In this paper, it is shown that short maneuverable arrays can achieve a high fraction of usable bearing space for target detection in interference-dominated scenarios, despite their lower array gain against diffuse background noise. Two narrowband techniques are presented which use the expectation-maximization maximum likelihood algorithm under different models of the time-varying field directionality. The first, derivative based maximum likelihood, uses a deterministic model while the second, recursive Bayes maximum likelihood, uses a stochastic model for the time-varying spatial spectrum. In addition, a broadband extension is introduced that incorporates temporal spectral knowledge to suppress ambiguities when the average sensor array spacing is greater than a half-wavelength. Dynamic multi-source simulations demonstrate the ability of a short, maneuvering array to reduce array ambiguities and spatial grating lobes in an interference dominated environment. Monte Carlo evaluation of receiver operating characteristics is used to evaluate the improvement in source detection achieved by the proposed methods versus conventional broadband beamforming.

A waveguide invariant adaptive matched filter for active sonar target depth classification.

This paper addresses depth discrimination of a water column target from bottom clutter discretes in wideband active sonar. To facilitate classification, the waveguide invariant property is used to derive multiple snapshots by uniformly sub-sampling the short-time Fourier transform (STFT) coefficients of a single ping of wideband active sonar data. The sub-sampled target snapshots are used to define a waveguide invariant spectral density matrix (WI-SDM), which allows the application of adaptive matched-filtering based approaches for target depth classification. Depth classification is achieved using a waveguide invariant minimum variance filter (WI-MVF) which matches the observed WI-SDM to depth-dependent signal replica vectors generated from a normal mode model. Robustness to environmental mismatch is achieved by adding environmental perturbation constraints (EPC) derived from signal covariance matrices averaged over the uncertain channel parameters. Simulation and real data results from the SCARAB98 and CLUTTER09 experiments in the Mediterranean Sea are presented to illustrate the approach. Receiver operating characteristics (ROC) for robust waveguide invariant depth classification approaches are presented which illustrate performance under uncertain environmental conditions.

Time-varying spatial spectrum estimation with a maneuverable towed array.

This paper addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow-ship maneuvers. In this paper, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution toward endfire. The Cramér-Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: (1) Maximum likelihood (ML) estimation solved using the expectation maximization algorithm and (2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics are presented to evaluate the algorithms' detection performance versus signal-to-noise ratio. The results indicate that both FDM algorithms offer the potential to provide superior detection performance when compared to conventional beamforming with a maneuverable array.

Waveguide invariant broadband target detection and reverberation estimation.

Reverberation often limits the performance of active sonar systems. In particular, backscatter off of a rough ocean floor can obscure target returns and/or large bottom scatterers can be easily confused with water column targets of interest. Conventional active sonar detection involves constant false alarm rate (CFAR) normalization of the reverberation return which does not account for the frequency-selective fading caused by multipath propagation. This paper presents an alternative to conventional reverberation estimation motivated by striations observed in time-frequency analysis of active sonar data. A mathematical model for these reverberation striations is derived using waveguide invariant theory. This model is then used to motivate waveguide invariant reverberation estimation which involves averaging the time-frequency spectrum along these striations. An evaluation of this reverberation estimate using real Mediterranean data is given and its use in a generalized likelihood ratio test based CFAR detector is demonstrated. CFAR detection using waveguide invariant reverberation estimates is shown to outperform conventional cell-averaged and frequency-invariant CFAR detection methods in shallow water environments producing strong reverberation returns which exhibit the described striations.

Waveguide invariant focusing for broadband beamforming in an oceanic waveguide.

The performance of broadband sonar array processing can degrade significantly in shallow-water environments when interference becomes angularly spread due to multipath propagation. Particularly for towed line arrays near endfire, elevation angle spreading of multipath interference often results in masking of weaker sources of interest. While adaptive beamforming in a series of narrow frequency bands can suppress coherent multipath interference, this approach requires long observation times to estimate the required narrowband covariance matrices. To form wideband covariance matrices which can be estimated with less observation time, plane-wave focusing methods have been used to avoid interference covariance matrix rank inflation. This paper extends wideband focusing to the case of coherent multipath interference. The approach presented here, called waveguide invariant focusing (WIF), exploits a robust relationship for the frequency dependence of horizontal wave number differences. Unlike matched-field methods, WIF does not model multipath wave fronts but rather makes the interference appear to occupy the same rank-one subspace across frequency. This permits formation of wideband covariance matrices without interference rank inflation. Simulation experiments in a realistic ocean environment indicate that adaptive beamforming using WIF covariance matrices can provide a significant array gain improvement over conventional adaptive methods with limited observation time.

Isolation and minimization of head motion-induced signal variations in fMRI data using independent component analysis.

Task-related head movement during acquisition of fMRI data represents a serious confound for both motion correction and estimates of task-related activation. Cost functions implemented in most conventional motion-correction algorithms compare two volumes for similarity but fail to account for signal variability that is not due to motion (e.g., brain activation). We therefore recently proposed the theoretical basis for a novel method for fMRI motion correction, termed motion-corrected independent component analysis (MCICA), that allows for brain activation present in an fMRI time-series to be implicitly modeled and mitigates motion-induced signal changes without having to directly estimate the motion parameters (Liao et al., IEEE Transactions on Medical Imaging 2005;25:29-44). To explore the effects of non-movement-related signal changes on registration error, we performed several previously proposed test simulations (Freire et al., IEEE Transactions on Medical Imaging 2002;21:470-484) to evaluate the performance of MCICA and compare it with the conventional square-of-difference-based measures such as LS-SPM and LS-AIR. We demonstrate that for both simulated data and real fMRI images, the proposed MCICA method performs favorably. Specifically, in simulations MCICA was more robust to the addition of simulated activation, and did not lead to the detection of false activations after correction for simulated task-correlated motion. With actual data from a motor fMRI experiment, the time course of the derived continually task-related ICA component became more correlated with the underlying behavioral task after preprocessing with MCICA compared to other methods, and the associated activation map was more clustered in the primary motor and supplementary motor cortices without spurious activation at the brain edge. We conclude that assessing the statistical properties of a motion-corrupted volume in relation to other volumes in the series, as is done with MCICA, is an accurate means of differentiating between motion-induced signal changes and other sources of variability in fMRI data.

An information-theoretic criterion for intrasubject alignment of FMRI time series: motion corrected independent component analysis.

A three-dimensional image registration method for motion correction of functional magnetic resonance imaging (fMRI) time-series, based on independent component analysis (ICA), is described. We argue that movement during fMRI data acquisition results in a simultaneous increase in the joint entropy of the observed time-series and a decrease in the joint entropy of a nonlinear function of the derived spatially independent components calculated by ICA. We propose this entropy difference as a reliable criterion for motion correction and refer to a method that maximizes this as motion-corrected ICA (MCICA). Specifically, a given motion-corrupted volume may be corrected by determining the linear combination of spatial transformations of the motion-corrupted volume that maximizes the proposed criterion. In essence, MCICA consists of designing an adaptive spatial resampling filter which maintains maximum temporal independence among the recovered components. In contrast with conventional registration methods, MCICA does not require registration of motion-corrupted volumes to a single reference volume which can introduce artifacts because corrections are applied without accounting for variability due to the task-related activation. Simulations demonstrate that MCICA is robust to activation level, additive noise, random motion in the reference volumes and the exact number of independent components extracted. When the method was applied to real data with minimal estimated motion, the method had little effect and, hence, did not introduce spurious changes in the data. However, in a data series from a motor fMRI experiment with larger motion, preprocessing the data with the proposed method resulted in the emergence of activation in primary motor and supplementary motor cortices. Although mutual information (MI) was not explicitly optimized, the MI between all subsequent volumes and the first one was consistently increased for all volumes after preprocessing the data with MCICA. We suggest MCICA represents a robust and reliable method for preprocessing of fMRI time-series corrupted with motion.

Matched-field depth estimation for active sonar.

This work concerns the problem of estimating the depth of a submerged scatterer in a shallow-water ocean by using an active sonar and a horizontal receiver array. As in passive matched-field processing (MFP) techniques, numerical modeling of multipath propagation is used to facilitate localization. However, unlike passive MFP methods where estimation of source range is critically dependent on relative modal phase modeling, in active sonar source range is approximately known from travel-time measurements. Thus the proposed matched-field depth estimation (MFDE) method does not require knowledge of the complex relative multipath amplitudes which also depend on the unknown scatterer characteristics. Depth localization is achieved by modeling depth-dependent relative delays and elevation angle spreads between multipaths. A maximum likelihood depth estimate is derived under the assumption that returns from a sequence of pings are uncorrelated and the scatterer is at constant depth. The Cramér-Rao lower bound on depth estimation mean-square-error is computed and compared with Monte Carlo simulation results for a typical range-dependent, shallow-water Mediterranean environment. Depth estimation performance to within 10% of the water column depth is predicted at signal-to-noise ratios of greater than 10 dB. Real data results are reported for depth estimation of an echo repeater to within 10-m accuracy in this same shallow water environment.

The performance of matched-field track-before-detect methods using shallow-water Pacific data.

Matched-field track-before-detect processing, which extends the concept of matched-field processing to include modeling of the source dynamics, has recently emerged as a promising approach for maintaining the track of a moving source. In this paper, optimal Bayesian and minimum variance beamforming track-before-detect algorithms which incorporate a priori knowledge of the source dynamics in addition to the underlying uncertainties in the ocean environment are presented. A Markov model is utilized for the source motion as a means of capturing the stochastic nature of the source dynamics without assuming uniform motion. In addition, the relationship between optimal Bayesian track-before-detect processing and minimum variance track-before-detect beamforming is examined, revealing how an optimal tracking philosophy may be used to guide the modification of existing beamforming techniques to incorporate track-before-detect capabilities. Further, the benefits of implementing an optimal approach over conventional methods are illustrated through application of these methods to shallow-water Pacific data collected as part of the SWellEX-1 experiment. The results show that incorporating Markovian dynamics for the source motion provides marked improvement in the ability to maintain target track without the use of a uniform velocity hypothesis.