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Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple adjacent targets with significant intensity differences. In this study, we propose a deconvolution algorithm based on a modified likelihood model of beamformed intensities (M-DCV) for estimation of the spatial intensity distribution. The simulated annealing iterative scheme is used to obtain the maximum likelihood estimation. An approximate expression based on the generalized negative binomial (GNB) distribution is introduced to calculate the conditional probability distribution of the beamformed intensity. The deconvolution algorithm is further simplified with an approximate likelihood model (AM-DCV) that can reduce the computational complexity for each iteration. We employ a direct deconvolution method based on the Fourier transform to enhance the initial solution, thereby reducing the number of iterations required for convergence. The M-DCV and AM-DCV algorithms are validated using synthetic and experimental data, demonstrating a maximum improvement of 73% in angular resolution and a sidelobe suppression of 15 dB. Experimental examples demonstrate that the imaging performance of the deconvolution algorithm based on a linear small-aperture array consisting of 16 array elements is comparable to that obtained through conventional beamforming using a linear large-aperture array consisting of 96 array elements. The proposed algorithm is applicable for Ocean Acoustic Waveguide Remote Sensing (OAWRS) and other sensing applications using linear arrays.

Ocean Acoustic Waveguide Remote Sensing (OAWRS) enables fish population density distributions to be instantaneously quantified and continuously monitored over wide areas. Returns from seafloor geology can also be received as background or clutter by OAWRS when insufficient fish populations are present in any region. Given the large spatial regions that fish inhabit and roam over, it is important to develop automatic methods for determining whether fish are present at any pixel in an OAWRS image so that their population distributions, migrations and behaviour can be efficiently analyzed and monitored in large data sets. Here, a statistically optimal automated approach for distinguishing fish from seafloor geology in OAWRS imagery is demonstrated with Neyman–Pearson hypothesis testing which provides the highest true-positive classification rate for a given false-positive rate. Multispectral OAWRS images of large herring shoals during spawning migration to Georges Bank are analyzed. Automated Neyman-Pearson hypothesis testing is shown to accurately distinguish fish from seafloor geology through their differing spectral responses at any space and time pixel in OAWRS imagery. These spectral differences are most dramatic in the vicinity of swimbladder resonances of the fish probed by OAWRS. When such significantly different spectral dependencies exist between fish and geologic scattering, the approach presented provides an instantaneous, reliable and statistically optimal means of automatically distinguishing fish from seafloor geology at any spatial pixel in wide-area OAWRS images. Employing Kullback–Leibler divergence or the relative entropy in bits from Information Theory is shown to also enable automatic discrimination of fish from seafloor by their distinct statistical scattering properties across sensing frequency, but without the statistical optimal properties of the Neyman–Pearson approach.

Norwegian spring-spawning herring are a critical economic resource for multiple nations in the North Atlantic and a keystone species of the Nordic Seas ecosystem. Given the wide areas that the herring occupy, it is difficult to accurately measure the population size and spatial distribution. Ocean Acoustic Waveguide Remote Sensing (OAWRS) was used to instantaneously measure the areal population density of Norwegian herring over more than one thousand square kilometers in spawning grounds near Ålesund, Norway. In the vicinity of the Ålesund trench near peak spawning, significant attenuation in signal-to-noise ratio and mean sensing range was observed after nautical sunset that had not been observed in previous OAWRS surveys in the Nordic Seas or in other regions. We show that this range-dependent decay along a given propagation path was caused by attenuation through dense herring shoals forming at sunset and persisting through the evening for transmissions near the swimbladder resonance peak. OAWRS transmissions are corrected for attenuation in a manner consistent with waveguide scattering theory and simultaneous downward directed local line-transect measurements in the region in order to produce instantaneous wide-area population density maps. Corresponding measured reductions in the median sensing range over the azimuth before ambient noise limitation are shown to be theoretically predictable from waveguide scattering theory and observed population densities. Spatial-temporal inhomogeneities in wide-area herring distributions seen synoptically in OAWRS imagery show that standard sparsely spaced line-transect surveys through this region during spawning can lead to large errors in the estimated population due to spatial and temporal undersampling.

Acoustics is the primary means of long-range and wide-area sensing in the ocean due to the severe attenuation of electromagnetic waves in seawater. While it is known that densely packed fish groups can attenuate acoustic signals during long-range propagation in an ocean waveguide, previous experimental demonstrations have been restricted to single line transect measurements of either transmission or backscatter and have not directly investigated wide-area sensing and communication issues. Here we experimentally show with wide-area sensing over 360° in the horizontal and ranges spanning many tens of kilometers that a single large fish shoal can significantly occlude acoustic sensing over entire sectors spanning more than 30° with corresponding decreases in detection ranges by roughly an order of magnitude. Such blockages can comprise significant impediments to underwater acoustic remote sensing and surveillance of underwater vehicles, marine life and geophysical phenomena as well as underwater communication. This makes it important to understand the relevant mechanisms and accurately predict attenuation from fish in long-range underwater acoustic sensing and communication. To do so, we apply an analytical theory derived from first principles for acoustic propagation and scattering through inhomogeneities in an ocean waveguide to model propagation through fish shoals. In previous experiments, either the attenuation from fish in the shoal or the scattering cross sections of fish in the shoal were measured but not both, making it impossible to directly confirm a theoretical prediction on attenuation through the shoal. Here, both measurements have been made and they experimentally confirm the waveguide theory presented. We find experimentally and theoretically that attenuation can be significant when the sensing frequency is near the resonance frequency of the shoaling fish. Negligible attenuation was observed in previous low-frequency ocean acoustic waveguide remote sensing (OAWRS) experiments because the sensing frequency was sufficiently far from the swimbladder resonance peak of the shoaling fish or the packing densities of the fish shoals were not sufficiently high. We show that common heuristic approaches that employ free space scattering assumptions for attenuation from fish groups can lead to significant errors for applications involving long-range waveguide propagation and scattering.

The migration of extensive social groups towards specific spawning grounds in vast and diverse ocean environments is an integral part of the regular spawning process of many oceanic fish species. Oceanic fish in such migrations typically seek locations with environmental parameters that maximize the probability of successful spawning and egg/larval survival. The 3D spatio-temporal dynamics of these behavioral processes are largely unknown due to technical difficulties in sensing the ocean environment over wide areas. Here, we use ocean acoustic waveguide remote sensing (OAWRS) to instantaneously image immense herring groups over continental-shelf-scale areas at the Georges Bank spawning ground. Via multi-spectral OAWRS measurements, we capture a shift in swimbladder resonance peak correlated with the herring groups’ up-slope spawning migration, enabling 3D spatial behavioral dynamics to be instantaneously inferred over thousands of square kilometers. We show that herring groups maintain near-bottom vertical distributions with negative buoyancy throughout the migration. We find a spatial correlation greater than 0.9 between the average herring group depth and corresponding seafloor depth for migratory paths along the bathymetric gradient. This is consistent with herring groups maintaining near-seafloor paths to both search for optimal spawning conditions and reduce the risk of predator attacks during the migration to shallower waters where near-surface predators are more dangerous. This analysis shows that multi-spectral resonance sensing with OAWRS can be used as an effective tool to instantaneously image and continuously monitor the behavioral dynamics of swimbladder-bearing fish group behavior in three spatial dimensions over continental-shelf scales.

Ocean Acoustic Waveguide Remote Sensing (OAWRS) has recently been shown to be capable of instantaneously imaging and continuously monitoring fish populations over continental shelf-scale areas, covering thousands of km². We show how OAWRS can be used in a variety of oceanic ecosystems to remotely assess populations and study the behavior of fish and other marine organisms, such as Antarctic krill, to help the study of marine ecology and the ecosystem-based approach to fisheries management.

Wide area acoustic remote sensing often involves the use of coherent receiver arrays to determine the spatial distribution of sources and scatterers at any instant. The resulting acoustic intensity images are typically corrupted by signal-dependent noise from Gaussian random field fluctuations arising from the central limit theorem and have a spatial resolution that depends on the incident direction, sensing array aperture and wavelength. Here, we use the maximum likelihood method to deconvolve the intensity distribution measured on a coherent line array assuming a discrete angular distribution of incident plane waves. Instantaneous wide area population density images of fish aggregations measured with Ocean Acoustic Waveguide Remote Sensing (OAWRS) are deconvolved to illustrate the effectiveness of this approach in improving angular resolution over conventional planewave beamforming.

Recently reported declines in the population of Atlantic cod have led to calls for additional survey methods for stock assessments. In combination with conventional line-transect methods that may have ambiguities in sampling fish populations, Ocean Acoustic Waveguide Remote Sensing (OAWRS) has been shown to have a potential for providing accurate stock assessments (Makris N.C., et al. Science 2009, 323, 1,734–1,737; 54th Northeast Regional Stock Assessment Workshop (54th SAW) US Department of Commerce, Northeast Fisheries Science Center, 2012). The use of OAWRS technology enables instantaneous wide-area sensing of fish aggregations over thousands of square kilometers. The ratio of the intensity of scattered returns from fish versus the seafloor in any resolution cell typically determines the maximum fish detection range of OAWRS, which then is a function of fish population density, scattering amplitude and depth distribution, as well as the level of seafloor scattering. With the knowledge of oceanographic parameters, such as bathymetry, sound speed structure and attenuation, we find that a Rayleigh–Born volume scattering approach can be used to efficiently and accurately estimate seafloor scattering over wide areas. From hundreds of OAWRS measurements of seafloor scattering, we determine the Rayleigh–Born scattering amplitude of the seafloor, which we find has a ƒ2,4 frequency dependence below roughly 2 kHz in typical continental shelf environments along the US northeast coast. We then find that it is possible to robustly detect cod aggregations across frequencies at and near swim bladder resonance for observed spawningconfigurations along the U.S. northeast coast, roughly the two octave range 150–600 Hzfor water depths up to roughly 100 m. This frequency range is also optimal for long-rangeocean acoustic waveguide propagation, because it enables multimodal acoustic waveguidepropagation with minimal acoustic absorption and forward scattering losses. As the sensingfrequency moves away from the resonance peak, OAWRS detection of cod becomesincreasingly less optimal, due to a rapid decrease in cod scattering amplitude. In otherenvironments where cod depth may be greater, the optimal frequencies for cod detectionare expected to increase with swim bladder resonance frequency.