Model-based signal processing techniques have been developed over the years to improve the capability of active and passive sonar systems for detecting and localizing quiet underwater targets. In a generic matched-field processor, hydrophone signals measured at the array are compared to hypothetical signals (replicas) that are calculated by a full-field acoustic model for a given target position. This matching is carried out for many potential target locations within a search region (range, depth and bearing) to form an ambiguity surface whose peak values provide the greatest likelihood that targets are present. In this paper, we evaluate a version of a matched-field processor that combines measured data with a higher-order parabolic equation (PE) algorithm to effectively backpropagate an (unnormalized) ambiguity surface outwards from the receiving array. To illustrate this PE-based method, the unconventional processor is applied to some synthetic and experimental hydrophone data received on vertical line arrays in shallow-water waveguides.
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