Source localization in range and depth in an Arctic environment

Fizell, Richard G.; Wales, Stephen C.

doi:10.1121/1.2022889

Cited by 21 publications

(9 citation statements)

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“…The "conventional" MFP was essentially proposed by Bucker and then implemented by Fizell [14,34]. Since then it has been used ill virtually every article on MFP.…”

Section: Conventional Mfpmentioning

confidence: 99%

“…The MVDF algorithm is now defined as BMV( A) when the sample covariance is used instead of the ensemble covariance, or (34) There are several points here. First, is the assumption that sample covariance can be used in place of an ensemble one.…”

Section: Minimum Variance Distortionless Filter Mfpmentioning

confidence: 99%

“…[110] The first experimental demonstration of MFP using field data was reported by Fizell and Wales. [34] They used low frequency data (47 lIz) recorded on a vertical array suspended from the Arctic ice pack at the FRA1I IV ice camp in 1982. They were able to localize a source at a second ice camp 260 km distant to a precision consistent with the satellite navigation of the camps.…”

Section: Matched Field Processing -Historical Overviewmentioning

confidence: 99%

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Matched Field Processing in Ocean Acoustics

Baggeroer

Kuperman

1993

Acoustic Signal Processing for Ocean Exploration

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ABSTRACT. Matched field processing (MFP) is a generalized beam forming method which uses the spatial complexities of acoustic fields in an ocean wavegnide to localize sources in range, depth and azimuth or to infer parameters of the waveguide it.self. It has experimentally localized sources with accuracies exceeding the Rayleigh limit for depth and the Fresnel limit for range by two orders of magnitude. MFP exploits the coherence of the modefmultipath structure and it is especially effective at low frequencies where the ocean supports coherent propagation over very loug ranges. This contrasts with plane wave based models which are degraded by modal and lUultipath phenomena and are generally ineffective when waveguide phenomena are important. MFP is a spatial matched fLlter where the correlation signal, or replica, is determined by the Green's function of the waveguide. It can have either conventional or adaptive formulations and it has been implemented with an a.-;sortment of both narrowband and wideband signal models. All involve some form of correlation betwcen the replicas deri ved from the wave equation and the data measured at an array of sensors. Since the replica gcnerally has a complicated dependence upon the source location and environmental parameters, the wave eqnation mllst be solved over this parameter space. One can view MFP as an inverse problem where one attempts to invert for these dependencies over the parameter space of the source and the environment. There is cUlTently a large literature discussing many theoretical aspects of MFP and this is supported by numerous simulations; several experiments acquiring data for MFP now have been conducted in several ocean environments and these have demonstrated both its capabilities and some of its limitations. Consequently, there is a modest understanding of both the theory and the experimental capabilities of MFP. This article provides an oVl!fvicw of both.

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“…The "conventional" MFP was essentially proposed by Bucker and then implemented by Fizell [14,34]. Since then it has been used ill virtually every article on MFP.…”

Section: Conventional Mfpmentioning

confidence: 99%

Section: Minimum Variance Distortionless Filter Mfpmentioning

confidence: 99%

Section: Matched Field Processing -Historical Overviewmentioning

confidence: 99%

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Matched Field Processing in Ocean Acoustics

Baggeroer

Kuperman

1993

Acoustic Signal Processing for Ocean Exploration

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“…Following the success of adaptive beamforming, the same principles were later applied to matched field replica vectors by Fizell and Wales (1985) and . In a related work, source motion compensation was studied by Zurk et al (2003).…”

Section: Matched Field Processingmentioning

confidence: 99%

Advances in Aquatic Target Localization with Passive Sonar

Gebbie¹

2000

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New underwater passive sonar techniques are developed for enhancing target localization capabilities in shallow ocean environments. The ocean surface and the seabed act as acoustic mirrors that reflect sound created by boats or subsurface vehicles, which gives rise to echoes that can be heard by hydrophone receivers (underwater microphones). The goal of this work is to leverage this "multipath" phenomenon in new ways to determine the origin of the sound, and thus the location of the target. However, this is difficult for propeller driven vehicles because the noise they produce is both random and continuous in time, which complicates its measurement and analysis. Further, autonomous underwater vehicles (AUVs) pose additional challenges because very little is known about the sound they generate, and its similarity to that of boats. Existing methods for localizing propeller noise using multiple hy- iii

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“…The Conventional Matched-Field Processing (CMFP) algorithm was first proposed by Bucker [l] and first implemented by Fizell [2]. To determine the location of a source, Bucker's MFP algorithm compares the measured pressure of signals at a receiver to the theoretical transmissions at the source.…”

Section: Introductionmentioning

confidence: 99%

High-performance embedded computing for Conventional Matched-Field Processing

Kim

George

Conference Proceedings of the 2003 IEEE International

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Advanced sonar algorithms with complex-wave propagation processing are of critical importance in the field of acoustic signal processing, as they possess the ability to localize signal sources more precisely in a cluttered environment.Conventional Matched-Field Processing (CMFP) is such an algorithm that provides range and depth results by employing environmental parameters. However, the enhancement of features is limited by the extensive computational and memory requirements of the algorithm even for a small problem size. High-performance computing, when applied to matched-field processing on a microprocessor-based distributed system, can provide solutions for this challenging problem in performance, scalability, and cost. Based on domain decomposition techniques, two parallel algorithms for matched-field processing are introduced in this paper for in-situ signal processing. Performance results collected on low-power distributed embedded systems are presented in terms of execution times and parallel efficiencies. The results of these analyses demonstrate that parallel in-situ processing holds the potential to meet the needs of matched-field processing in a scalable fashion.

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Source localization in range and depth in an Arctic environment

Cited by 21 publications

References 0 publications

Matched Field Processing in Ocean Acoustics

Matched Field Processing in Ocean Acoustics

Advances in Aquatic Target Localization with Passive Sonar

High-performance embedded computing for Conventional Matched-Field Processing

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