A stable data-adaptive method for matched-field array processing in acoustic waveguides

Abstract

Capon’s maximum likelihood (ML) method has been used with some success in matched field processing for source range and depth estimation. One reason for the interest in the ML is that it is data adaptive; that is, it adapts to the actual noise field present rather than requiring an a priori estimate of the noise component for prewhitening. When modal noise is present the ML can become sensitive to any deviations from the unperturbed case (i.e., from the model) as would be introduced by phase errors or model parameter errors. Using a dimensionality-reduction procedure a more stable data adaptive method, the ‘‘reduced’’ ML (RML), is obtained. The RML is compared here with the ML on simulated data from a 21-sensor array in a Pekeris waveguide supporting eight normal modes. Under modal noise conditions the RML provides a significant improvement over ML when phase errors occur. Although the deviation from the model considered here is that caused by phase errors, the nature of the perturbation is not important since the sensitivity of ML is not to any special type of perturbation.