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>Z< The Performance of Matched-Field Beamformers with Mediterranean Vertical Array Data Jeffrey L. Krolik

Minimum variance (MV) adaptive beamforming has been widely proposed for matched-field processing because it provides a means of suppressing ambiguous beampattern sidelobes. A difficulty with MV methods, however, is their sensitivity to signal wavefront mismatch. In this work, the performance of the three robust MV methods and the Bartlett beamformer is evaluated using vertical array data from the Mediterranean Sea collected by the NATO SACLANT Centre. The three MV methods considered are: 1) the reduced MV beamformer (RMV), 2) the MV beamformer with neighborhood location constraints (MV-NLC), and 3) the MV beamformer with environmental perturbation constraints (MV-EPC). While the Bartlett, RMV, and MV-NLC methods assume the ocean environment is known precisely, the MV-EPC method models the environment as being random with known statistics. Experimental and companion simulation results indicate that for modest environmental uncertainty, the MV-EPC beamformer achieves a higher probability of correct localization and better sidelobe performance than the other three methods.

>Z< Robust Matched-Field Beamforming with Benchmark Shallow-Water Acoustic Array Data Jeffrey L. Krolik

In this paper, four robust matched-field beamforming methods are evaluated using benchmark shallow-water Mediterranean data. While the Bartlett beamformer, reduced minimum variance beamformer (RMV), and minimum variance beamformer with neighborhood location constraints (MV-NLC) assume a known ocean, the minimum variance beamformer with environmental perturbation constraints (MV-EPC) exploits a model for uncertainties in the ocean parameters. For modest environmental uncertainty, the MV-EPC beamformer is shown to achieve higher probability of correct localization with simulated data and better tracking and sidelobe performance with experimental data than the Bartlett, RMV, and MV-NLC methods.

>Z< Robust Maximum-Likelihood Source Localization in an Uncertain Shallow Water Waveguide J. Tabrikian, J.L. Krolik, and H. Messer

This paper addresses the problem of matched-field source localization in the presence of uncertainties in the ocean environment. Because signal wavefront mismatch can cause anomalous source location estimates, development of robust localization methods is critically important. In this paper, a robust Maximum-Likelihood estimator is proposed. It is based on a decomposition of the field into predictable and unpredictable subspaces of the acoustic normal mode representation. Identification of the predictable modes is made according to the second order joint statistics of the horizontal wavenumbers. The performance of the acoustic array data from the Mediterranean Sea. The algorithm has superior probability of correct localization than the Maximum-Likelihood, Matched-Mode-Processing, and Bartlett methods.

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