Kalman滤波在数据融合及资料同化上的应用

发布时间：2018-03-05 05:13

本文选题：Kalman滤波　切入点：有效波高　出处：《河南大学》2017年硕士论文　论文类型：学位论文

【摘要】：Kalman滤波作为最优化自回归滤波器的基础,具有算法便于计算机编程实现,并能对所获得的数据进行实时的更新和处理等优点,基于此,本文关于Kalman滤波做了以下研究:首先,利用Kalman滤波做了滤波前后海洋有效波高与海洋浮标数据的对比分析,来验证Kalman滤波的有效性及其最优估计的精确性.并借鉴前人的研究结果,改进Kalman滤波的迭代方程,提高其运算精度与速率.其次,为了提高海洋卫星数据后处理精度,提出一个新的数据融合方法—— Kalman滤波融合法.并选取HY-2, Jason-2和SARAL/AltiKa三颗海洋卫星所获得的海洋数据做融合,对比分析融合前后数据的方差和覆盖率,以此来证明该方法的有效性及应用的灵活性.接着,分别利用Kalman滤波和反距离加权法反演全球海洋电离层TEC,并对两种方法反演出的海洋卫星电离层TEC数据做误差分析.同时也再次突出了 Kalman滤波的实用性及平滑的特点.最后,本文基于Lorenz-96模型,分析研究了集合Kalman滤波在资料同化过程中几个重要参数的取值问题,具有重要的应用前景.
[Abstract]:As the foundation of the optimal autoregressive filter, Kalman filter is easy to realize by computer programming, and can update and process the obtained data in real time. Based on this, this paper does the following research on Kalman filter: first, The comparison and analysis of ocean effective wave height and ocean buoy data before and after filtering are done by using Kalman filter to verify the validity of Kalman filter and the accuracy of its optimal estimation. The iterative equation of Kalman filter is improved by using the previous research results. Secondly, in order to improve the post-processing accuracy of marine satellite data, a new data fusion method, Kalman filtering fusion method, is proposed, and ocean data obtained by HY-2, Jason-2 and SARAL/AltiKa are selected for fusion. The variance and coverage of the data before and after fusion are compared to demonstrate the effectiveness and flexibility of the method. Kalman filter and inverse distance weighting method are used to inverse the global ionospheric ionospheric TEC data respectively, and the error analysis of the two methods is made. At the same time, the practicability and smoothness of the Kalman filtering are also highlighted. Based on the Lorenz-96 model, this paper analyzes and studies the selection of several important parameters in the process of data assimilation by using the set Kalman filter, which has an important application prospect.
【学位授予单位】：河南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN713

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