Application Of Kalman Filter In Data Fusion And Data Assimilation

As the basis of an optimized autoregressive filter, Kalman filter has the following advantages, it is convenient for computer programming, and it can update and process the data in real-time. Base on this, we do the following research:Firstly, we compare the ocean significant wave height data before and after the Kalman filtering with the ocean buoy data, to verify the validity of the Kalman filter and the accuracy of its optimal estimation. From the previous results, we improve the Kalman filter iterative equation, to enhance its operation accuracy and rate.Secondly, in order to improve the accuracy of post-processing of marine satellite data,we propose a new method of data fusion——Kalman filter fusion method, and select the ocean data obtained from three satellite satellites of HY-2, Jason-2 and SARAL / AltiKa,and analyze the variance and coverage data before and after the integration, to prove the effectiveness of the method and the application. Then, we use Kalman filter and inverse distance weighting method to inverse the global oceanic ionosphere TEC respectively, and compare the accuracy of the inversion data. Once again this highlights the practical and smooth features of Kalman filter.Finally, based on the Lorenz-96 model, we study the value of several important parameters in the process of data assimilation, and has important application prospect.