LOS and NLOS Classification for Underwater Acoustic Localization

The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonli...

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Main Authors: DIAMANT, Roee, TAN, Hwee-Pink, LAMPE, Lutz
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Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2958
https://ink.library.smu.edu.sg/context/sis_research/article/3958/viewcontent/ToAFinal.pdf
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spelling sg-smu-ink.sis_research-39582016-01-28T07:29:00Z LOS and NLOS Classification for Underwater Acoustic Localization DIAMANT, Roee TAN, Hwee-Pink LAMPE, Lutz The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS. 2014-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2958 info:doi/10.1109/TMC.2012.249 https://ink.library.smu.edu.sg/context/sis_research/article/3958/viewcontent/ToAFinal.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Underwater acoustic localization (UWAL) line-of-sight nonline-of-sight time-of-arrival classification Computer and Systems Architecture Computer Engineering Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Underwater acoustic localization (UWAL)
line-of-sight
nonline-of-sight
time-of-arrival classification
Computer and Systems Architecture
Computer Engineering
Software Engineering
spellingShingle Underwater acoustic localization (UWAL)
line-of-sight
nonline-of-sight
time-of-arrival classification
Computer and Systems Architecture
Computer Engineering
Software Engineering
DIAMANT, Roee
TAN, Hwee-Pink
LAMPE, Lutz
LOS and NLOS Classification for Underwater Acoustic Localization
description The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS.
format text
author DIAMANT, Roee
TAN, Hwee-Pink
LAMPE, Lutz
author_facet DIAMANT, Roee
TAN, Hwee-Pink
LAMPE, Lutz
author_sort DIAMANT, Roee
title LOS and NLOS Classification for Underwater Acoustic Localization
title_short LOS and NLOS Classification for Underwater Acoustic Localization
title_full LOS and NLOS Classification for Underwater Acoustic Localization
title_fullStr LOS and NLOS Classification for Underwater Acoustic Localization
title_full_unstemmed LOS and NLOS Classification for Underwater Acoustic Localization
title_sort los and nlos classification for underwater acoustic localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2958
https://ink.library.smu.edu.sg/context/sis_research/article/3958/viewcontent/ToAFinal.pdf
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