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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。
In order to improve the localization of sound sources in an uncertain ocean environment, the Bayesian sound source localization method simultaneously inverts the environmental parameters and sound source locations. The method uses genetic algorithm to optimize in the parameter space and integrates the posterior probability density in the variation range of the environmental parameters to obtain the edge probability distribution of the sound source distance and depth, and obtains the optimal value of the sound source position from Uncertainty Analysis of Location Results. Taking into account the weak sensitivity of seabed density and attenuation coefficient to the matching field processing cost function, the indirect inversion of these two parameters is realized by using the empirical relationship between seafloor parameters. The experimental data of a Yellow Sea acoustic propagation in 2000 were processed and analyzed. The results show that the Bayesian acoustic source localization method has good tolerance to environmental mismatch. Empirical formula can be used to reduce the dimension parameters to be reversed to further improve the positioning accuracy.