In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density(PPD) over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy. In order to improve the ability to localize a source in an uncertain acoustic environment, a Bayesian approach, referred to here as Bayesian localization is used by including the environment in the parameter search space. Genetic algorithms are used for the parameter optimization. This method integrates the a posterior probability density (PPD) over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth, from which the most-probable source location and localization uncertainties can be extracted. Consumption of that seabed density and attenuation are less sensitive to the objective function of matched field processing, we utilize the empirical relationship to invert those parameters indirectly. The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed. It was found that the Bayesian localization method that incorporates the environmental variability into the processor, made it rob ust to the uncertainty in the ocean environment. In addition, using the empirical relationship could enhance the localization accuracy.