Environmental uncertainty is one of the limiting factors in the matched-field localization. Within a Bayesian framework, environmental focalization has been widely used to solve the augmented localization problem, in which the environmental parameters, source ranges and depths are considered to be the unknown variables. However, the position of the moving source varies with time, which limits the observation interval and the number of acoustic signals. Therefore, it has to estimate lots of unknown parameters with the limited observation information. When the source moves fast or the environment has great uncertainty, the environmental focalization gets worse. Taking the parameter estimation of Kalman filter in the non-stationary process as a reference, the acoustic signals from a series of observations are treated in a simultaneous inversion. This provides the most informative solution, since data from multiple source locations are brought to bear simultaneously on the environmental unknowns, which in turn constrain the source locations better. In this article, the time-unvarying parameters are introduced to describe the source position. The source positions are inverted indirectly by the time-unvarying parameters, which reduces the estimated parameter dimension effectively. At the same time, the current estimated results are treated as the priori information of the next inversion, which establishes the new prior distribution and cost function. It could compensate for some individual abnormal data effectively and realize continuous localization of the moving source. The noise signals radiated from a surface ship target are processed and analyzed. The Bayesian tracking algorithm greatly increases the observation interval and the number of acoustic signals, and enhances the localization accuracy in an uncertain water environment. Tracking results of the ship noise indicate that simultaneous inversion of multiple acoustic observations with constant velocity track model and the Thikhonov regularization provides a better solution than sequential independent inversions. It is indicated that the Bayesian tracking method learns the uncertain environment as more observations become available. It is discovered that the maximum a posteriori solution and the two-dimensional solution have similar results according to the global positioning system value. The reason is that the source locations are treated implicitly by the source speed, which is similar to the marginal probability distribution by reducing the multidimensional posterior probability density to the representative two-dimensional probability distributions.%环境参数失配导致定位性能大幅度下降是匹配场定位所面临的难题之一。应用贝叶斯理论对环境聚焦，是当前解决该难题的研究热点。环境聚焦方法的实质是将未知环境参数和声源位置联合优化估计。然而，运动声源的位置时变性限制了观测时间长度和观测信息量，因此不得不利用很有限的观测信息来实现众多参数的估计。当航速较快或是环境信息的不确定性较大时，环境聚焦方法的效果迅速变差。借鉴卡尔曼滤波处理非平稳过程的参数估计思想，对航速较恒定的声源，本文将多个时刻的接收信号同时反演，引入能够描述声源位置随时间变化规律的时不变参数，以较少的时不变参数间接反演多个声源位置，从而有效降低待估参数维数。同时将当前估计结果作为下一次反演的先验信息，建立新的先验分布和代价函数，有效补偿个别异常数据，实现运动声源的连续定位。该方法在相同的环境不确定条件下，大幅度增加了观测时间和观测信息量，可以较好地改善环境聚焦方法的定位效果。
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