Abstract
In applications of matched-field processing (MFP) to passive sonar, signals measured on a hydrophone array due to a source at some unknown position xs=(rs,zs) are correlated with signals predicted by a propagation model (replicas) due to a source at a given position x=(r,z). This matching is carried out for many assumed x’s within a search region (range, r and depth, z) to form a (normalized) ambiguity surface whose peak value provides an estimate of xs. For an N-element array, reciprocity is invoked to reduce the computational effort to computing N replica fields at each point on the search grid. In this paper, a matched-field processor proposed by Tappert et al. [J. Acoust. Soc. Am. 78, S85 (1985)] is revisited that combines measured data with PE starting fields to effectively backpropagate an (unnormalized) ambiguity surface outwards from the receiving array. This unnormalized processor generates the ambiguity surface N times faster than the normalized one. The effectiveness of the backpropagation algorithm is examined by applying it to some multi-tonal data obtained during the Hudson Canyon experiment. Issues relating to normalization and incoherent/coherent frequency averaging are discussed.
Published Version
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