General second-order covariance of Gaussian maximum likelihood estimates applied to passive source localization in fluctuating waveguides

Bertsatos, Ioannis; Zanolin, M.; Ratilal, Purnima; Chen, Tianrun; Makris, Nicholas C.

doi:10.1121/1.3488303

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…The model takes into account the environmental parameters, such as the rangedependent bathymetry, seafloor geo-acoustic properties [18], MM source and POAWRS receiver array location, and over 200 experimentally measured water-column sound speed profiles ( Figure 3 of Ref. [18]) to stochastically [2,74,75] compute the propagated acoustic intensities via MonteCarlo simulation using the approach outlined in Refs. [18,72,73,76,77].…”

Section: Estimating MM Detection Region For Poawrs Receiver Arraymentioning

confidence: 99%

Continental-shelf scale Passive Ocean Acoustic Waveguide Remote Sensing of marine mammals and other submerged objects including detection, localization, and classification

Wang¹

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In this thesis, we develop the basics of the Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS) technique for the instantaneous continental-shelf scale detection, localization and species classification of marine mammal vocalizations. POAWRS uses a large-aperture, densely sampled coherent hydrophone array system with orders of magnitude higher array gain to enhance signal-to-noise ratios (SNR) by coherent beamforming, enabling detection of underwater acoustic signals either two orders of magnitude more distant in range or lower in SNR than a single hy-

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Section: Estimating MM Detection Region For Poawrs Receiver Arraymentioning

confidence: 99%

Continental-shelf scale Passive Ocean Acoustic Waveguide Remote Sensing of marine mammals and other submerged objects including detection, localization, and classification

Wang¹

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Vast assembly of vocal marine mammals from diverse species on fish spawning ground

Wang

Garcia

Huang

et al. 2016

Nature

147

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Observing marine mammal (MM) populations continuously in time and space over the immense ocean areas they inhabit is challenging but essential for gathering an unambiguous record of their distribution, as well as understanding their behaviour and interaction with prey species. Here we use passive ocean acoustic waveguide remote sensing (POAWRS) in an important North Atlantic feeding ground to instantaneously detect, localize and classify MM vocalizations from diverse species over an approximately 100,000 km(2) region. More than eight species of vocal MMs are found to spatially converge on fish spawning areas containing massive densely populated herring shoals at night-time and diffuse herring distributions during daytime. We find the vocal MMs divide the enormous fish prey field into species-specific foraging areas with varying degrees of spatial overlap, maintained for at least two weeks of the herring spawning period. The recorded vocalization rates are diel (24 h)-dependent for all MM species, with some significantly more vocal at night and others more vocal during the day. The four key baleen whale species of the region: fin, humpback, blue and minke have vocalization rate trends that are highly correlated to trends in fish shoaling density and to each other over the diel cycle. These results reveal the temporospatial dynamics of combined multi-species MM foraging activities in the vicinity of an extensive fish prey field that forms a massive ecological hotspot, and would be unattainable with conventional methodologies. Understanding MM behaviour and distributions is essential for management of marine ecosystems and for accessing anthropogenic impacts on these protected marine species.

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A Unified Approach to Passive and Active Ocean Acoustic Waveguide Remote Sensing

Makris¹

2012

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Ocean acoustic waveguide remote sensing (OAWRS) is the basis for the primary undersea surveillance systems of the US Navy for both passive and active detection, localization, imaging and monitoring. Here OAWRS refers to all applications of acoustic remote sensing in an ocean waveguide. The primary objective of this work is to continue the data analysis and model development initiated in a series of major ocean acoustics experiments the PI has conducted, which has been summarized in an invited paper in IEEE Spectrum [1]. We applied fundamental physical and statistical principles inherent in waveguide propagation and scattering to advance the state of the art in undersea surveillance. The approach was to use the extensive data analysis, software and fundamental waveguide scattering, propagation, reverberation and statistical models developed by the PI, his collaborators, graduate students and subcontractors during the various experimental programs the PI has directed. WORK COMPLETED/RESULTS Bistatic, long-range measurements of acoustic scattered returns from vertically extended, air-filled cylindrical targets were made during three distinct field experiments [2-6] in fluctuating continental shelf environments. It is shown that Sonar Equation estimates of mean target-scattered intensity lead to large errors, differing by an order of magnitude from both the measurements and waveguide scattering theory. This is because the sonar equation approximation is not generally valid for targets with directional scatter functions in an ocean waveguide. The use of the Ingenito scattering model is also shown to lead to significant errors in estimating mean target-scattered intensity in the field experiments because they were conducted in range-dependent ocean environments with large variations in sound speed structure over the depth of the targets, scenarios that violate basic assumptions of the Ingenito model. A Greens' theorem based full-field model (VETWS) [7] that describes scattering from vertically extended cylindrical targets in range-dependent ocean waveguides by taking into account non-uniform sound speed structure over the target's depth extent is shown to accurately describe the statistics of the targets' scattered field in all three field experiments [8]. To account for the scintillation in the measured scattered intensity caused by fluctuations of the ocean waveguide [9,10], Monte-Carlo simulations of the scattered field are computed by implementing the full-field model in a rangedependent environment randomized by internal waves [9]. Returns from the man-made target are also 8. PERFORMING ORGANIZATION REPORT NUMBER

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General second-order covariance of Gaussian maximum likelihood estimates applied to passive source localization in fluctuating waveguides

Cited by 4 publications

References 32 publications

Continental-shelf scale Passive Ocean Acoustic Waveguide Remote Sensing of marine mammals and other submerged objects including detection, localization, and classification

Continental-shelf scale Passive Ocean Acoustic Waveguide Remote Sensing of marine mammals and other submerged objects including detection, localization, and classification

Vast assembly of vocal marine mammals from diverse species on fish spawning ground

A Unified Approach to Passive and Active Ocean Acoustic Waveguide Remote Sensing

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