Data driven source localization using a library of nearby shipping sources of opportunity

Durofchalk, Nicholas C.; Jin, Jihui; Vazquez, Heriberto J.; Gemba, Kay L.; Romberg, Justin; Sabra, Karim G.

doi:10.1121/10.0009083

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Also, physics-based signal processing algorithms typically used to further extract information [16], [25] require manual parameter tuning or trial and error such that they are not sufficiently scalable. Additionally, many of the machine learning approaches that have been developed for the underwater acoustics domain exclusively perform or assist in either the signal of interest detection task [12], [13], [49] or a regression task that uses the detected signals of interest as input [17], [50], [51]. A true solution to the analysis burden imposed by massive amounts of PAM data will require methods that are robust to experimental data and can both 1) extract information related to the environment or source from signals of interest and 2) identify the signals of interest in large acoustic data.…”

Section: The Curse Of Big Acoustic Datamentioning

confidence: 99%

Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors

Goldwater

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Underwater acoustics is a powerful tool for learning about the ocean’s soniferous marine life. However, most modern acoustic sensing systems consist of expensive arrays of timesynchronized recorders which require a crewed research vessel and significant expertise to deploy, operate, and recover. Recently, there has been a growing corpus of research related to algorithms for low-cost and accessible acoustic hardware. Deep learning methods have shown great promise when applied to underwater acoustics inverse problems. While many signal processing or physics-based algorithms exhibit long run times and require manual labor to extract signals of interest, tune parameters, as well as visually verify the results, an appropriately trained neural network can quickly process data with no human supervision. Both low-cost passive acoustic monitoring (PAM) sensing platforms and algorithms that can analyze massive amounts of raw data are critical to accessible and scalable approaches in ocean acoustic monitoring. This thesis presents a method for detection and 2D (latitude-longitude) localization of underwater acoustic sources without requiring synchronized sensors. The signals of interest here are the dispersive low-frequency impulsive gunshot vocalizations of North Pacific and North Atlantic right whales (NPRWs, NARWs). In shallow-water channels, the timefrequency representation of the received signal is strongly dependent on source-receiver range, making these impulses ideal candidates for range-based localization. The first step in the localization pipeline uses a temporal convolutional network (TCN) to simultaneously detect gunshot vocalizations and predict their ranges. Trained on spectrograms of synthetic data simulated in a variety of environments, the TCN is applied to PAM data from moorings in the Bering Sea. Gunshots are detected with high precision, and the range estimates are comparable to those estimated using traditional physics-based processing. Both methods use a minimal set of a priori environmental information including water column depth, sound speed, and density. Depending on the sensor layout, the TCN may need to scan large windows of data, so the number of unique acoustic sources is unknown. To automatically associate and localize range measurements, the proposed method seeks subsets of measurements across unique sensors which are internally consistent. For every considered measurement subset, locations are estimated with single constituent measurements left out and checked to be sufficiently close to the excluded measurement’s set of potential locations. If a measurement subset is entirely consistent in this manner, the measurements are added as neighboring nodes in a graph-based representation, and strongly connected components are used to determine data associations and calculate the final source location estimates. Informed by the methods developed in this thesis, an array of low-cost TOSSIT moorings was deployed in Cape Cod Bay and used to collect experimental PAM data. The localization results are comparable to another similar physics-based inversion approach. Overall, this thesis aims to fill a gap in acoustic data processing methods where data from a low-cost network of unsynchronized acoustic sensors are fused to localize acoustic sources. The presented methods and data processing pipeline demonstrate the great potential of low-cost acoustic sensing systems.

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Section: The Curse Of Big Acoustic Datamentioning

confidence: 99%

Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors

Goldwater

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Unsupervised Domain Adaptation for Source Localization Using Ships of Opportunity With a Deep Vertical Line Array

Liu,

Niu,

et al. 2024

IEEE J. Oceanic Eng.

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End-to-End Geoacoustic Inversion With Neural Networks in Shallow Water Using a Single Hydrophone

Vardi,

Bonnel

2024

IEEE J. Oceanic Eng.

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Data driven source localization using a library of nearby shipping sources of opportunity

Cited by 7 publications

References 17 publications

Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors

Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors

Unsupervised Domain Adaptation for Source Localization Using Ships of Opportunity With a Deep Vertical Line Array

End-to-End Geoacoustic Inversion With Neural Networks in Shallow Water Using a Single Hydrophone

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