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讨论了一种适用于浅海的时变声速剖面跟踪方法。该方法将时变水体声速剖面的反演问题建模为由描述声速剖面时变特性的状态方程与包含声压场局部测量信息的测量方程组成的状态-空间模型,提出利用自回归分析拟合方法将声速场扰动建模为高阶自回归演化模型,并通过集合卡尔曼滤波序贯地估计时间演化的海洋声速场。利用2001年亚洲海实验环境与声速测量数据,仿真分析了基于高阶自回归演化模型的时变声速剖面集合卡尔曼滤波估计方法。结果表明,相比于利用传统随机游走状态演化模型的估计方法,该改进方法可有效降低声速的跟踪误差,并且在较低信噪比条件下仍具有较好的跟踪性能。
A time-varying sound velocity profile tracking method suitable for shallow sea is discussed. This method models the inversion problem of the time-varying water body sound velocity profile as a state-space model that consists of a state equation describing the time-varying characteristics of the sound velocity profile and a measurement equation containing the local measurement information of the sound pressure field, The method models the sonic velocity perturbation as a high-order autoregressive evolution model, and sequentially estimates the time-evolutioned oceanic sonic velocity field by a set Kalman filter. Based on the 2001 Asian maritime experimental environment and sound velocity measurement data, a set of Kalman filter estimation methods based on the high-order autoregressive evolution model is presented. The results show that the proposed method can effectively reduce the tracking error of sound velocity and has better tracking performance under the condition of lower signal-to-noise ratio than that of the traditional random walk state evolution model.