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Christoph F. Mecklenbräuker, RUB (Germany)
Peter Gerstoft, SACLANTCEN (Italy)
Pei-Jung Chung, COMNETS (Germany)
Johann F. Böhme, RUB (Germany)
A generalized likelihood ratio test is considered for testing acoustic environmental models with application to parameter inversion using an acoustic propagation code. In the following, we use the term ``hierarchy of models'' to denote a sequence of model structures M_1, M_2,ldots in which each particular model structure M_n contains all previous ones as special cases. We propose a combined parameter estimation and multiple sequential test for simultaneously determining the model order and its parameters: given the observed data, how many parameters should be included in the model? The last question is important for the order selection problem in hierarchies of models with increasing number of parameters where the observations are corrupted by additive noise. Monte Carlo simulations show the behaviour of the sequential test for selecting a model order as a function of the SNR. Finally, the test is applied to broadband data measured using a vertical array near the island of Elba in the Mediterranean Sea.
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Maria João Rendas, I3S/CNRS (France)
Georges Bienvenu, Thomson Marconi Sonar (France)
In this paper we present a new technique for the evaluation/selection procedures of genetic algorithms, to be used in the context of parameter estimation problems. The proposed algorithm uses a priori information about the structure of the surface of which an extremum is being searched. For parameter estimation problems, the availability, at each iteration of a genetic algorithm, of a collection of samples of the ambiguity surface of the problem, enables the determination of the correlation between the observed ambiguity surface (at the sampled points) and the predicted ambiguity surface. The consideration of this information allows early detection of secondary extrema (which yield an ambiguity surface which does not correlate well with the observed one) and thus contributes to speed the convergence of the algorithm to the global optimal values. The paper applies the proposed technique to a source localization problem.
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Kerem Harmanci, Duke University (U.S.A.)
Jeffrey L. Krolik, Duke University (U.S.A.)
Matched-field beamforming has been proposed for localizing wideband acoustic sources in uncertain underwater channels. While adaptive matched-field beamforming provides adequate sidelobe suppression for stronger sources, at low signal-to-noise ratios it converges to its quiescent response, in this case the Bartlett beamformer, which has unacceptably high sidelobe levels. In this paper, a design method is presented for reducing matched-field non-adaptive beamformer sidelobe levels given a sufficiently large observation time-bandwidth product. The proposed (alpha)-beamformer incoherently averages narrowband matched-field beamformer output power over the signal band after a trade-off has been performed at each frequency to achieve better sidelobe suppression at the expense of some reduction in gain against diffuse noise. Simulations and results with Mediterranean vertical array data indicate that the wideband (alpha)-beamformer can provide improved sidelobe suppression versus conventional techniques.
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Brian F. Harrison, NUWCDIVNPT (U.S.A.)
Richard J. Vaccaro, University of Rhode Island (U.S.A.)
Donald W. Tufts, University of Rhode Island (U.S.A.)
In many estimation problems, the set of unknown parameters can be divided into a subset of desired parameters and a subset of nuisance parameters. Using a maximum a posteriori (MAP) approach to parameter estimation, these nuisance parameters are integrated out in the estimation process. This can result in an extremely computationally-intensive estimator. This paper proposes a method by which computationally-intensive integrations over the nuisance parameters required in Bayesian estimation may be avoided under certain conditions. The propsed method is an approximate MAP estimator which is much more computationally efficient than direct, or even Monte Carlo, integration of the joint posteriori distribution of the desired and nuisance parameters. As an example of its efficiency, we apply the fast algorithm to matched-field source localization in an uncertain environment.
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Donald F. Gingras, Naval Command, Control and Ocean Surveillance Center (U.S.A.)
Peter Gerstoft, SACLANT (Italy)
Neil L. Gerr, Office of Naval Research (U.S.A.)
Christoph F. Mecklenbräuker, Vienna University of Technology (Austria)
Matched field processing (MFP) refers to signal and array processing techniques in which, rather than a planewave arrival model, complex-valued (amplitude and phase) field predictions for propagating signals are used. Matched field processing has been successfully applied in ocean acoustics. In this paper the extension of MFP to the electromagnetic domain, i.e., electromagnetic (EM) MFP (EM-MFP) is described. Simulations of EM-MFP in the tropospheric setting suggest that, under suitable conditions, EM-MFP methods can enable EM sources to be both detected/localized and used as sources of opportunity for estimating the environmental parameters that determine EM propagation.
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