不确知海洋环境下的贝叶斯声源定位法

为了提高不确知海洋环境下的声源定位性能,贝叶斯声源定位法将环境参数与声源位置同时反演。该方法利用遗传算法在参数空间中寻优,将后验概率密度在环境参数起伏变化范围内积分,得到声源距离和深度的边缘概率分布,从中求得声源位置的最优值,并进行定位结果的不确定性分析。考虑到海底密度和衰减系数对匹配场处理代价函数的敏感性较弱,利用海底参数之间的经验关系实现这两个参数的间接反演。处理并分析了2000年的一次黄海声传播实验数据,研究表明,贝叶斯声源定位法对环境失配有较好的宽容性。采用经验公式可减少待反演参量维数,进一步提高定位的精度。 

Bayesian localization in an uncertain ocean environment

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, make it robust to the uncertainty in the ocean environment. In addition, using the empirical relationship could enhance the localization accuracy.