Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment.
Matched-Field Processing (MFP) and Matched-Mode Processing (MMP) are two popular techniques for passively localizing an underwater acoustic emitter in range and depth. One major drawback of these techniques has been their sensitivity to uncertainty concerning the acoustic environment. Several method...
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ftdtic:ADA313862 2023-05-15T15:38:47+02:00 Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. Pierce, David D. NAVAL POSTGRADUATE SCHOOL MONTEREY CA 1996-06 text/html http://www.dtic.mil/docs/citations/ADA313862 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA313862 en eng http://www.dtic.mil/docs/citations/ADA313862 APPROVED FOR PUBLIC RELEASE DTIC AND NTIS Acoustic Detection and Detectors Physical and Dynamic Oceanography *UNDERWATER ACOUSTICS *BARENTS SEA *PASSIVE SONAR MATHEMATICAL MODELS ALGORITHMS SIGNAL PROCESSING UNCERTAINTY SIGNAL TO NOISE RATIO ANGLE OF ARRIVAL DIRECTION FINDING THESES HIGH RESOLUTION MATHEMATICAL FILTERS WAVE PROPAGATION SOUND TRANSMISSION ACOUSTIC MEASUREMENT NOISE REDUCTION ACOUSTIC FILTERS SOUND WAVES ORDER STATISTICS OCEAN ENVIRONMENTS SONAR RECEIVERS ACOUSTIC FIELDS UNDERWATER SOUND SIGNALS SOUND RANGING DEPTH FINDING MATCHED FIELD PROCESSING Text 1996 ftdtic 2016-02-19T18:57:35Z Matched-Field Processing (MFP) and Matched-Mode Processing (MMP) are two popular techniques for passively localizing an underwater acoustic emitter in range and depth. One major drawback of these techniques has been their sensitivity to uncertainty concerning the acoustic environment. Several methods for addressing this phenomenon have been proposed in the literature, with varying degrees of success. Achieving high-quality location estimates remains a problem except in simple range-independent experiments or numerical simulations. In this study, we demonstrate an approach for robust, accurate emitter localization in a highly range-dependent real environment using MMP. The main factors contributing to successful localization are: 1) use of the high-resolution Multiple Signal Classification (MUSIC) algorithm, which performs well even when only a few robust modes can be obtained by mode filtering; and 2) use of an acoustic propagation model incorporating mode coupling, which is able to generate accurate replica fields in a strongly range-dependent environment. A secondary objective of the study was to demonstrate the application of higher-order statistical estimation techniques to reduce noise effects. Our results indicate that these techniques show unacceptable sensitivity to noise- and model-induced estimation errors and require further refinement before they will be useful in the underwater acoustic localization problem. Text Barents Sea Defense Technical Information Center: DTIC Technical Reports database Barents Sea |
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Defense Technical Information Center: DTIC Technical Reports database |
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language |
English |
topic |
Acoustic Detection and Detectors Physical and Dynamic Oceanography *UNDERWATER ACOUSTICS *BARENTS SEA *PASSIVE SONAR MATHEMATICAL MODELS ALGORITHMS SIGNAL PROCESSING UNCERTAINTY SIGNAL TO NOISE RATIO ANGLE OF ARRIVAL DIRECTION FINDING THESES HIGH RESOLUTION MATHEMATICAL FILTERS WAVE PROPAGATION SOUND TRANSMISSION ACOUSTIC MEASUREMENT NOISE REDUCTION ACOUSTIC FILTERS SOUND WAVES ORDER STATISTICS OCEAN ENVIRONMENTS SONAR RECEIVERS ACOUSTIC FIELDS UNDERWATER SOUND SIGNALS SOUND RANGING DEPTH FINDING MATCHED FIELD PROCESSING |
spellingShingle |
Acoustic Detection and Detectors Physical and Dynamic Oceanography *UNDERWATER ACOUSTICS *BARENTS SEA *PASSIVE SONAR MATHEMATICAL MODELS ALGORITHMS SIGNAL PROCESSING UNCERTAINTY SIGNAL TO NOISE RATIO ANGLE OF ARRIVAL DIRECTION FINDING THESES HIGH RESOLUTION MATHEMATICAL FILTERS WAVE PROPAGATION SOUND TRANSMISSION ACOUSTIC MEASUREMENT NOISE REDUCTION ACOUSTIC FILTERS SOUND WAVES ORDER STATISTICS OCEAN ENVIRONMENTS SONAR RECEIVERS ACOUSTIC FIELDS UNDERWATER SOUND SIGNALS SOUND RANGING DEPTH FINDING MATCHED FIELD PROCESSING Pierce, David D. Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. |
topic_facet |
Acoustic Detection and Detectors Physical and Dynamic Oceanography *UNDERWATER ACOUSTICS *BARENTS SEA *PASSIVE SONAR MATHEMATICAL MODELS ALGORITHMS SIGNAL PROCESSING UNCERTAINTY SIGNAL TO NOISE RATIO ANGLE OF ARRIVAL DIRECTION FINDING THESES HIGH RESOLUTION MATHEMATICAL FILTERS WAVE PROPAGATION SOUND TRANSMISSION ACOUSTIC MEASUREMENT NOISE REDUCTION ACOUSTIC FILTERS SOUND WAVES ORDER STATISTICS OCEAN ENVIRONMENTS SONAR RECEIVERS ACOUSTIC FIELDS UNDERWATER SOUND SIGNALS SOUND RANGING DEPTH FINDING MATCHED FIELD PROCESSING |
description |
Matched-Field Processing (MFP) and Matched-Mode Processing (MMP) are two popular techniques for passively localizing an underwater acoustic emitter in range and depth. One major drawback of these techniques has been their sensitivity to uncertainty concerning the acoustic environment. Several methods for addressing this phenomenon have been proposed in the literature, with varying degrees of success. Achieving high-quality location estimates remains a problem except in simple range-independent experiments or numerical simulations. In this study, we demonstrate an approach for robust, accurate emitter localization in a highly range-dependent real environment using MMP. The main factors contributing to successful localization are: 1) use of the high-resolution Multiple Signal Classification (MUSIC) algorithm, which performs well even when only a few robust modes can be obtained by mode filtering; and 2) use of an acoustic propagation model incorporating mode coupling, which is able to generate accurate replica fields in a strongly range-dependent environment. A secondary objective of the study was to demonstrate the application of higher-order statistical estimation techniques to reduce noise effects. Our results indicate that these techniques show unacceptable sensitivity to noise- and model-induced estimation errors and require further refinement before they will be useful in the underwater acoustic localization problem. |
author2 |
NAVAL POSTGRADUATE SCHOOL MONTEREY CA |
format |
Text |
author |
Pierce, David D. |
author_facet |
Pierce, David D. |
author_sort |
Pierce, David D. |
title |
Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. |
title_short |
Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. |
title_full |
Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. |
title_fullStr |
Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. |
title_full_unstemmed |
Range-Dependent Passive Source Localization Using Data from the Barents Sea Tomography Experiment. |
title_sort |
range-dependent passive source localization using data from the barents sea tomography experiment. |
publishDate |
1996 |
url |
http://www.dtic.mil/docs/citations/ADA313862 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA313862 |
geographic |
Barents Sea |
geographic_facet |
Barents Sea |
genre |
Barents Sea |
genre_facet |
Barents Sea |
op_source |
DTIC AND NTIS |
op_relation |
http://www.dtic.mil/docs/citations/ADA313862 |
op_rights |
APPROVED FOR PUBLIC RELEASE |
_version_ |
1766370122202611712 |