Adaptive matched field processing in an uncertain propagation environment

Preisig, James C.

doi:10.1575/1912/5493

Cited by 10 publications

(10 citation statements)

References 35 publications

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“…Prcisig has used a generalized minmax approach to formulate a very elegant theory for minmax MFP. [80] He minimized the maximum value of an estimate of the field over a range of environmental uncertainties in the environment. In one sense his approach cau be interpreted as a "joint replica estimate for MFP with MVDF" which attempts to couple MCM methods and focalization.…”

Section: Other Mfp Algorithmsmentioning

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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Section: Other Mfp Algorithmsmentioning

confidence: 99%

“…Environmental mismatch has been studied early in great detail, [79,94,29,40,80). Environmental mismatch refers to uncertainty in the propagation model, e.g.…”

Section: Mismatch Studiesmentioning

confidence: 99%

Matched Field Processing in Ocean Acoustics

Baggeroer

Kuperman

1993

Acoustic Signal Processing for Ocean Exploration

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“…The application of this approach to the development of optimal minmax estimators is discussed more fully in [5].…”

Section: V-285mentioning

confidence: 99%

Optimal minmax estimation and the development of minmax estimation error bounds

Preisig

1992

[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing

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It is often desired to create an optimal estimator of some parameter 6' given the observation 2. However, the relationship between 6' and z may depend on another parameter 4 which is unknown to the processor and not of direct interest. In this case, an estimator which performs well for one value of 4 may perform poorly for another value of 4. One approach to dealing with this problem is to develop an estimator whose worst case performance evaluated over some range of 4 is as good as possible. Such an optimal minmax estimator is derived. The derivation of this estimator also motivates an approach to developing lower bounds on the minmax estimation error achievable by any estimator.

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“…In this paper, the relationship of the Adaptive Minmax Matched Field Processor to both a particular type of Wiener filter and a particular realization of the Minimum Variance Distortionless Response array processor (MVDR) [2,3] (also referred to as the Maximum Likelihood array processor) is derived. The Wiener filter relationship is used in Section II to develop an efficient algorithm for implementing the matched field processor.…”

Section: Adaptive Matched Field Processing In Anmentioning

confidence: 99%

“…The implementation and analysis of the processor proposed in this paper are presented in [4]. While the processor has its foundation in the framework of minmax signal processing, it can also be shown to be equivalent to both a particular type of Wiener filter and a combined signal model estimator and Minimum Variance Distortionless Response array processor (MVDR) [5, 6] (also referred to as the Maximum Likelihood array processor).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Array Processing in Uncertain Inhomogeneous Media

Oppenehim¹,

Baggeroer²

1992

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Adaptive matched field processing in an uncertain propagation environment

Cited by 10 publications

References 35 publications

Matched Field Processing in Ocean Acoustics

Matched Field Processing in Ocean Acoustics

Optimal minmax estimation and the development of minmax estimation error bounds

Adaptive Array Processing in Uncertain Inhomogeneous Media

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