Locating multiple underwater acoustic sources is a problem that can be solved using antenna array beamforming based on the matched field (MF) processing. However, known MF beamforming techniques fail to provide good performance for multiple sources, a high noise power, and/or when the sources are close to each other. This paper proposes an MF technique for solving the localization problem. The proposed technique exploits formulation of the localization problem in terms of sparse representation of a small number of source positions among a much larger number of potential positions. The sparse representation is formulated as the basis pursuit de-noising (BPDN) problem for complex-valued variables. The solution is found as a joint solution to a set of BPDN problems corresponding to the set of source frequencies subject to the joint support. The joint BPDN problem is efficiently solved using the Homotopy approach and coordinate descent search. For further reduction in the complexity, a position grid refinement method is applied. Using simulated and real experimental data, it is shown that the technique can provide accurate source localization for multiple sources. The proposed technique outperforms other MF techniques in resolving sources positioned closely to each other, tolerance to the noise and capability of locating multiple sources.

Broadband Underwater Localization of Multiple Sources Using Basis Pursuit De-Noising

In regions of the world where tuberculosis (TB) poses the greatest disease burden, the lack of access to skilled laboratories is a significant problem. A lab-free method for assessing patient recovery during treatment would be of great benefit, particularly for identifying patients who may have drug-resistant tuberculosis. We hypothesize that cough analysis may provide such a test. In this paper we describe algorithm development in support of a pilot study of TB patient coughing. We describe several approaches to event detection and classification, and show preliminary data which suggest that cough count decreases after the start of treatment in drug-responsive patients. Our eventual goal is development of a low-cost ambulatory cough analysis system that will help identify patients with drug-resistant tuberculosis.

Cough detection algorithm for monitoring patient recovery from pulmonary tuberculosis

Background 
A laboratory-free test for assessing recovery from pulmonary tuberculosis (TB) would be extremely beneficial in regions of the world where laboratory facilities are lacking. Our hypothesis is that analysis of cough sound recordings may provide such a test. In the current paper, we present validation of a cough analysis tool.


Methodology/Principal Findings 
Cough data was collected from a cohort of TB patients in Lima, Peru and 25.5 hours of recordings were manually annotated by clinical staff. Analysis software was developed and validated by comparison to manual scoring. Because many patients cough in bursts, coughing was characterized in terms of cough epochs. Our software correctly detects 75.5% of cough episodes with a specificity of 99.6% (comparable to past results using the same definition) and a median false positive rate of 4 false positives/hour, due to the noisy, real-world nature of our dataset. We then manually review detected coughs to eliminate false positives, in effect using the algorithm as a pre-screening tool that reduces reviewing time to roughly 5% of the recording length. This cough analysis approach provides a foundation to support larger-scale studies of coughing rates over time for TB patients undergoing treatment.

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Validation of an Automated Cough Detection Algorithm for Tracking Recovery of Pulmonary Tuberculosis Patients

Deep vertical line arrays can exploit the reliable acoustic path (RAP), which provides low transmission loss (TL) for targets at moderate ranges, and increased TL for distant interferers. However, nearby surface interference also has favorable RAP propagation and cannot be separated from a submerged target without horizontal aperture. In this work, a physics-based Fourier transform variant is introduced, which achieves depth-based signal separation by exploiting the spatial structure resulting from the coherent addition of the direct and surface-reflected propagation paths present for submerged sources. Simulation results demonstrate depth-based signal separation without requiring knowledge of the ocean environment.

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Depth-based signal separation with vertical line arrays in the deep ocean

In passive sonar, narrowband adaptive beamforming techniques can be exploited to increase the signal-to-interference-plus-noise ratio (SINR), providing that array steering vector (ASV) errors and cross-spectral density matrix (CSDM) estimation errors can be controlled. When beamforming large aperture, many-element arrays in dynamic scenarios, the number of stationary snapshots available for CSDM estimation can be small compared to the number of array elements, leading to the problem of snapshot deficiency. Furthermore, common narrowband approaches become computationally prohibitive for large bandwidths. Here, we exploit the wideband nature of passive sonar signals to alleviate snapshot deficiency and reduce computational complexity. Narrowband robust Capon beamformers (RCBs), which exploit ellipsoidal ASV uncertainty sets to maintain high SINR, are extended to the wideband problem via the steered covariance matrix (STCM) method, yielding wideband RCBs (WBRCBs). To further reduce computational complexity and speed up algorithm convergence, subarray techniques are also incorporated, yielding wideband subarray RCBs (WBSARCBs). These algorithms, which are applicable to arbitrary array geometries, are evaluated using simulated and experimental passive sonar data.

Wideband Robust Capon Beamforming for Passive Sonar

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.

/pdf/source-motion-mitigation-for-adaptive-matched-field-3awh0ratya.pdf

Source motion mitigation for adaptive matched field processing

This paper evaluates the performance of several reduced-rank, adaptive matched field processing (AMFP) algorithms for passive sonar detection in a shallow-water environment. Effective rank reduction improves the stability of adaptive beamformer weight calculation when the number of available snapshots is limited. Here, rank-reduction techniques with various criteria for subspace selection are evaluated within a common framework and compared to the full-rank conventional and minimum-variance (MVDR) beamformers. Results from real data demonstrate that rank reduction, properly applied can improve AMFP detection performance in practical system implementations.

/pdf/evaluation-of-reduced-rank-adaptive-matched-field-processing-2kzv62k4ah.pdf

Evaluation of reduced-rank, adaptive matched field processing algorithms for passive sonar detection in a shallow-water environment

Multipath propagation in shallow water can lead to mismatch losses when single-path replicas are used for horizontal array beamforming. Matched field processing (MFP) seeks to remedy this by using full-field acoustic propagation models to predict the multipath arrival structure. Ideally, MFP can give source localization in range and depth as well as detection gains, but robustly estimating range and depth is difficult in practice. The approach described here seeks to collapse full-field replica outputs to bearing, which is robustly estimated, while retaining any signal gains provided by the full-field model. Cluster analysis is used to group together full-field replicas with similar responses. This yields a less redundant “sampled field” describing a set of representative multipath structures for each bearing. A detection algorithm is introduced that uses clustering to collapse beamformer outputs to bearing such that signal gains are retained while increases in the noise floor are minimized. Horizontal ar...

Cluster analysis and robust use of full-field models for sonar beamforming

In this paper we demonstrate passive detection and localization in a noisy shallow water environment using matched field processing (MFP) with data obtained during the Santa Barbara Channel Experiment (SBCX). The use of an Adaptive MFP algorithm provides the ability to detect a submerged source in the presence of strong surface interference and also reduces ambiguity surface sidelobe clutter. We also consider the effects of source motion during the observation interval. Target motion can degrade signal gains by introducing, smearing across MFP range cells. For large arrays, such as those deployed during SBCX, motion can also introduce errors due to differential doppler across the array. Since long observation times are desirable for increased noise gain and to provide sample support for the adaptive algorithms, motion compensation is required. An approach is described that uses a velocity hypothesis to focus the snapshots prior to covariance estimation. Results show that with compensation, localization accuracy is improved and the full resolution of the array can be realized.

/pdf/3d-adaptive-matched-field-processing-for-a-moving-source-in-33x1rh6yww.pdf

3D adaptive matched field processing for a moving source in a shallow water channel

While matched field processing (MFP) algorithms have most commonly been applied to data collected on vertical line arrays, there is increasing interest in applying MFP to more general array geometries. One challenge for novel array design is to determine the fundamental performance of volumetric arrays in a shallow water channel. To address this issue, recent work by Tantum and Nolte [J. Acoust. Soc. Am. 107, 2101–2111 (2000)] has been extended to allow more general array geometries, including horizontal arrays steered away from endfire. The correlation among acoustic modes across the array can be related to MFP resolution and sidelobe levels. Another trend in underwater array design is the consideration of larger arrays which provide additional array gain. However, large arrays are susceptible to the effects of motion [Zurk et al., Oceans 99]. A new time series simulation that rigorously treats source and receiver motion is used to quantify the effects on adaptive MFP. Simulation results are compared to ...

Nigel Lee

Papers

Source motion mitigation for adaptive matched field processing

Evaluation of reduced-rank, adaptive matched field processing algorithms for passive sonar detection in a shallow-water environment

Cluster analysis and robust use of full-field models for sonar beamforming

3D adaptive matched field processing for a moving source in a shallow water channel

Array design and motion effects for matched field processing