Improving underwater localization accuracy with machine learning

Machine learning classification and regression algorithms were applied to calibrate the localization errors of a time-difference-of-arrival (TDOA)-based acoustic sensor array used for tracking salmon passage through a hydroelectric dam on the Snake River, Washington, USA. The locations of stationary...

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Veröffentlicht in:Review of scientific instruments 2018-07, Vol.89 (7), p.074902-074902
Hauptverfasser: Rauchenstein, Lynn T., Vishnu, Abhinav, Li, Xinya, Deng, Zhiqun Daniel
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container_issue 7
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container_title Review of scientific instruments
container_volume 89
creator Rauchenstein, Lynn T.
Vishnu, Abhinav
Li, Xinya
Deng, Zhiqun Daniel
description Machine learning classification and regression algorithms were applied to calibrate the localization errors of a time-difference-of-arrival (TDOA)-based acoustic sensor array used for tracking salmon passage through a hydroelectric dam on the Snake River, Washington, USA. The locations of stationary and mobile acoustic tags were first tracked using the approximate maximum likelihood algorithm. Next, ensembles of classification trees successfully identified and filtered data points with large localization errors. This prefiltering step allowed the creation of a machine-learned regression model function, which decreased the median distance error by 50% for the stationary tracks and by 34% for the mobile tracks. It also extended the previous range of sub-meter localization accuracy from 100 m to 250 m horizontal distance from the dam face (the receivers). Median distance errors in the depth direction were especially decreased, falling from 0.49 m to 0.04 m in the stationary tracks and from 0.38 m to 0.07 m in the mobile tracks. These methods would have application to the calibration of error in any TDOA-based sensor network with a steady environment and array configuration.
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source AIP Journals Complete; Alma/SFX Local Collection
subjects acoustic sensors
Algorithms
Artificial intelligence
Classification
Damsites
Data points
Error detection
Hydroelectric dams
hydrophone
Localization
Machine learning
OTHER INSTRUMENTATION
power plants
regression analysis
Regression models
Salmon
Scientific apparatus & instruments
Sensor arrays
signal processing
sound-emitting devices
speed of sound
telemetry
water transportation
title Improving underwater localization accuracy with machine learning
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