Fastmap: a fast, approximate maximum a posteriori probability parameter estimator with application to robust matched-field processing

In many estimation problems, the set of unknown parameters can be divided into a subset of desired parameters and a subset of nuisance parameters. Using a maximum a posteriori (MAP) approach to parameter estimation, these nuisance parameters are integrated out in the estimation process. This can result in an extremely computationally-intensive estimator. This paper proposes a method by which computationally-intensive integrations over the nuisance parameters required in Bayesian estimation may be avoided under certain conditions. The propsed method is an approximate MAP estimator which is much more computationally e cient than direct, or even Monte Carlo, integration of the joint posteriori distribution of the desired and nuisance parameters. As an example of its e ciency, we apply the fast algorithm to matchedeld source localization in an uncertain environment.