Robust matched-field localization in uncertain ocean environments

Abstract

Matched-field source localization methods can be sensitive to environmental parameter mismatch. A statistically optimal approach to source localization in the presence of environmental uncertainty is the maximum a posteriori probability (MAP) estimator. Unfortunately, practical implementation of the MAP estimator results in a computationally intensive processor. In this paper, a localization technique is presented that is a computationally efficient approximation to the MAP estimator. A two-step search procedure is used to estimate source position. The first step utilizes an approximation to the MAP estimator which allows much of the computation to be computed efficiently off-line. This step also includes a computationally efficient range-depth smoothing which provides robustness to grid density. In step two, ambiguities arising from step one are resolved using a fine-grid search procedure over the source location parameters. Simulations using the NRL Workshop benchmark environment, which has seven uncertain environmental parameters, show the performance of the technique to be comparable to that of the MAP estimator while requiring only a fraction of the computations.