Kay L. Gemba

Adaptive and compressive matched field processing

Matched field processing is a generalized beamforming method that matches received array data to a dictionary of replica vectors in order to locate one or more sources. Its solution set is sparse since there are considerably fewer sources than replicas. Using compressive sensing (CS) implemented using basis pursuit, the matched field problem is reformulated as an underdetermined, convex optimization problem. CS estimates the unknown source amplitudes using the replica dictionary to best explain the data, subject to a row-sparsity constraint. This constraint selects the best matching replicas within the dictionary when using multiple observations and/or frequencies. For a single source, theory and simulations show that the performance of CS and the Bartlett processor are equivalent for any number of snapshots. Contrary to most adaptive processors, CS also can accommodate coherent sources. For a single and multiple incoherent sources, simulations indicate that CS offers modest localization performance improvement over the adaptive white noise constraint processor. SWellEx-96 experiment data results show comparable performance for both processors when localizing a weaker source in the presence of a stronger source. Moreover, CS often displays less ambiguity, demonstrating it is robust to data-replica mismatch.

Partial dereverberation used to characterize open circuit scuba diver signatures

The use of passive acoustics to detect self-contained underwater breathing apparatus (SCUBA) divers is useful for nearshore and port security applications. While the performance of a detector can be optimized by understanding the signals spectral characteristics, anechoic recording environments are generally not available or are cost prohibitive. A practical solution is to obtain the source spectra by equalizing the recording with the inverse of the channels impulse response. This paper presents a dereverberation method for signal characterization that is subsequently applied to four recorded SCUBA configurations. The inverse impulse response is computed in the least-square sense, and partial dereverberation of SCUBA is performed over the 6-18 kHz band. Results indicate that early reflections and late reverberation added as much as 6.8 dB of energy. Mean unadjusted sound pressure levels computed over the 0.3-80 kHz band were 130 ± 5.9 dB re 1 μPa at 1 m. Bubble noise carries a significant amount of the total energy and masks the regulator signatures from 1.3 to 6 kHz, depending on the regulator configuration. While the dereverberation method is applied here to SCUBA signals, it is generally applicable to other sources if the impulse response of the recording environment can be obtained separately.

Multi-frequency sparse Bayesian learning for robust matched field processing

The multi-snapshot, multi-frequency sparse Bayesian learning (SBL) processor is derived and its performance compared to the Bartlett, minimum variance distortionless response, and white noise constraint processors for the matched field processing application. The two-source model and data scenario of interest includes realistic mismatch implemented in the form of array tilt and data snapshots not exactly corresponding to the range-depth grid of the replica vectors. Results demonstrate that SBL behaves similar to an adaptive processor when localizing a weaker source in the presence of a stronger source, is robust to mismatch, and exhibits improved localization performance when compared to the other processors. Unlike the basis or matching pursuit methods, SBL automatically determines sparsity and its solution can be interpreted as an ambiguity surface. Because of its computational efficiency and performance, SBL is practical for applications requiring adaptive and robust processing.

Sparse Bayesian learning with uncertain sensing matrix

Sparse Bayesian learning is a sparse processing method used for solving high-dimensional, underdetermined linear equations. Often the sensing matrix in the system of equations is assumed known and in presence of perturbations in this matrix performance of sparse processing degrades. We develop a sparse Bayesian learning method that accounts for perturbations in the sensing matrix. We derive an iterative weight update by performing evidence maximization. Beamforming simulations are used to demonstrate the advantages of the proposed method.

A tomography experiment using ships as sources of opportunity

An experiment was performed in the Santa Barbara Channel using four vertical acoustic receive arrays placed between the sea lanes of in- and outgoing shipping traffic. The purpose of the experiment was to determine whether these sources of opportunity can be utilized for tomographic inversion of water column properties. The environment was continuously monitored throughout the duration of the experiment. Ship tracks were obtained from the Automatic Identification System (AIS). Processing was developed to extract relative time delays between the arrays from the ships’ random radiation fields. This information, together with AIS constraints were used for inversion. Initial results are presented that also include an error analysis of the inversion.

Multi-frequency sparse Bayesian learning for matched field processing in non-stationary noise

Using simulations and data, we localize a quiet source in the presence of an interferer. The SWellEx-96 Event S59 consists of a submerged source towed along an isobath over a 65 min duration with an interferer traversing the source track. This range independent, multi-frequency scenario includes mismatch, non-stationary noise, and operational uncertainty. Mismatch is defined as a misalignment between the actual source field observed at the array and the modeled replica vector. The noise process changes likely with time. This is modelled as a heteroscedastic Gaussian process, meaning that the noise variance is non-stationary across snapshots. Sparse Bayesian learning (SBL) has been applied previously to the matched field processing application [Gemba et al, J. Acoust. Soc. Am., 141:3411-3420, 2017]. Results demonstrate that SBL exhibits desirable robustness properties and improved localization performance when compared to the white noise constraint and Bartlett processors.Using simulations and data, we localize a quiet source in the presence of an interferer. The SWellEx-96 Event S59 consists of a submerged source towed along an isobath over a 65 min duration with an interferer traversing the source track. This range independent, multi-frequency scenario includes mismatch, non-stationary noise, and operational uncertainty. Mismatch is defined as a misalignment between the actual source field observed at the array and the modeled replica vector. The noise process changes likely with time. This is modelled as a heteroscedastic Gaussian process, meaning that the noise variance is non-stationary across snapshots. Sparse Bayesian learning (SBL) has been applied previously to the matched field processing application [Gemba et al, J. Acoust. Soc. Am., 141:3411-3420, 2017]. Results demonstrate that SBL exhibits desirable robustness properties and improved localization performance when compared to the white noise constraint and Bartlett processors.

Estimating relative channel impulse responses from ships of opportunity in a shallow water environment

The uncertainty of estimating relative channel impulse responses (CIRs) obtained using the radiated signature from a ship of opportunity is investigated. The ship observations were taken during a 1.4 km (11 min) transect in a shallow water environment during the Noise Correlation 2009 (NC09) experiment. Beamforming on the angle associated with the direct ray-path yields an estimate of the ship signature, subsequently used in a matched filter. Relative CIRs are estimated every 2.5 s independently at three vertical line arrays (VLAs). The relative arrival-time uncertainty is inversely proportional to source bandwidth and CIR signal-to-noise ratio, and reached a minimum standard deviation of 5 μs (equivalent to approximately 1 cm spatial displacement). Time-series of direct-path relative arrival-times are constructed for each VLA element across the 11 min observation interval. The overall structure of these time-series compares favorably with that predicted from an array element localization model. The short-term standard deviations calculated on the direct-path (7 μs) and bottom-reflected-path (17 μs) time-series are in agreement with the predicted arrival-time accuracies. The implications of these observed arrival-time accuracies in the context of estimating sound speed perturbations and bottom-depth are discussed.

Using ships of opportunity for array element localization and relative channel impulse response estimation

The uncertainty of estimating relative channel impulse responses (CIRs) obtained using the radiated signature from a ship of opportunity is investigated. The ship observations were taken during a 1.4 km (11 min) transect during the Noise Correlation 2009 (NC09) experiment. Beamforming on the angle associated with the direct ray-path yields an estimate of the ship signature, subsequently used as a matched filter. Relative CIRs are estimated every 2.5 s independently at three vertical line arrays (VLAs) for a total of 270 observations per VLA. The estimated relative arrival-time uncertainty is inversely proportional to source bandwidth and CIR signal-to-noise ratio, and reached a minimum standard deviation of 5 μs (approximately 1 cm). The direct-path relative arrival-times are used to construct time series for each VLA element across the 11 min observation interval. The overall structure of these time series compares favorably with that predicted from an array element localization (AEL) model that exhibits sensitivity on the order of centimeters. The short-term standard deviations calculated on direct-path (7 μs) and bottom-reflected-path (17 μs) time series are in agreement with the estimated arrival-time accuracies. The implication of these observed arrival-time accuracies in the context of making sound speed perturbation and bottom-depth estimates is discussed.The uncertainty of estimating relative channel impulse responses (CIRs) obtained using the radiated signature from a ship of opportunity is investigated. The ship observations were taken during a 1.4 km (11 min) transect during the Noise Correlation 2009 (NC09) experiment. Beamforming on the angle associated with the direct ray-path yields an estimate of the ship signature, subsequently used as a matched filter. Relative CIRs are estimated every 2.5 s independently at three vertical line arrays (VLAs) for a total of 270 observations per VLA. The estimated relative arrival-time uncertainty is inversely proportional to source bandwidth and CIR signal-to-noise ratio, and reached a minimum standard deviation of 5 μs (approximately 1 cm). The direct-path relative arrival-times are used to construct time series for each VLA element across the 11 min observation interval. The overall structure of these time series compares favorably with that predicted from an array element localization (AEL) model that exhibits...

DOA estimation in heteroscedastic noise

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), inspiring stochastic maximum likelihood DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. This SBL approach is more flexible and performs better than high-resolution methods since they cannot estimate the heteroscedastic noise process. An alternative to SBL is simple data normalization, whereby only the phase across the array is utilized. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.

Extracting tomographic arrival time information from ships of opportunity in the Santa Barbara channel using blind deconvolution

This paper summarizes the ongoing investigations surrounding the use of a ray-based blind deconvolution algorithm to recover arrival time information from sources of opportunity, such as shipping vessels, recorded on vertical line arrays (VLAs) in ocean waveguides. The deconvolution is primarily performed by using an estimate of the unknown source phase, obtained through wideband beamforming, to essentially match filter the VLA recordings and recover the channel impulse response (CIR). This paper will focus on results from an experiment performed in 2016 in the Santa Barbara shipping channel (water depth ~550 m). Four VLAs, with both short (~15 m) and long (~56 m) apertures, were deployed between the north and south bound shipping lanes and continuously collected acoustic data during one week. With the ultimate goal of passive acoustic tomography in mind, this paper aims to discuss (1) the robustness of the algorithm to extract differential arrival times along VLA elements using ships as sources of opport...