集合卡尔曼滤波在时变声速剖面追踪中的性能分析

对集合卡尔曼滤波在时变海洋环境下的声速剖面追踪性能进行了分析。将南海实验背景下普林斯顿海洋模型预报的声速剖面正交分解为3阶系数组成的状态-空间形式,其状态转移方程建模为3阶自回归过程;基于卡尔曼反馈理论,利用适合于水平非均匀模型RAM仿真的观测声压场对系统状态进行校正,实现声速剖面的动态追踪。在水平均匀、水平非均匀和海底参数失配环境下的仿真结果均能较好地实现对声速剖面的追踪,验证了算法的可行性。同时对不同信噪比、粒子数、阵元数和海底参数失配等情况下的分析表明,观测信息量的增加可以有效抑制观测误差和模型误差的影响,相关结论得到了实验数据的验证。 

Performances of the ensemble Kalman filter on the tracking of time-evolving sound speed profiles

The performances of the Ensemble Kalman Filter (EnKF) on the tracking of time-evolving Sound Speed Profiles (SSPs) is studied.Firstly,based on the empirical orthogonal decomposition,the SSPs provided by the Princeton Ocean Model (POM) at the background of an actual experiment in the South China Sea are formulated as the form of state-space with the first three coefficients and the state equation is modeled as a three-orders auto-regress process.Then,according to the theory of Kalman filter,the dynamic tracking is implemented by the correction step where Range-dependent Acoustic Model (RAM) simulated acoustic filed is applied to correct the predicted state.Simulations in range-independent,range-dependent and geoacoustic parameters mismatched environments show good tracking results,which verifies the feasibility of the algorithm.In addition,the performances of EnKF with respect to Signal Noise Ratio (SNR),ensemble number,number of hydrophones and mismatched geoacoustic parameters are investigated.It shows that the increase of observations can efficiently reduce the effects of errors in both observing and modeling,which is confirmed by actual experiment data and provides an important reference for the realistic applications.