Kalman Filter and Its Application in Data Assimilation

In 1960, R.E. Kalman published his famous paper describing a recursive solution, the Kalman filter, to the discrete-data linear filtering problem. In the following decades, thanks to the continuous progress of numerical computing, as well as the increasing demand for weather prediction, target tracking, and many other problems, the Kalman filter has gradually become one of the most important tools in science and engineering. With the continuous improvement of its theory, the Kalman filter and its derivative algorithms have become one of the core algorithms in optimal estimation. This paper attempts to systematically collect and sort out the basic principles of the Kalman filter and some of its important derivative algorithms (mainly including the Extended Kalman filter (EKF), the Unscented Kalman filter (UKF), the Ensemble Kalman filter (EnKF)), as well as the scope of their application, and also to compare their advantages and limitations. In addition, because there are a large number of applications based on the Kalman filter in data assimilation, this paper also provides examples and classifies the applications of both the Kalman filter and its derivative algorithms in the field of data assimilation.

Automated summary

In 1960, R.E. Kalman published a paper introducing the Kalman filter, which provides a recursive solution to the discrete-data linear filtering problem. Over the years, the Kalman filter has become one of the most important tools in science and engineering due to the advancement of numerical computing and the increasing demand for various applications such as weather prediction and target tracking. This paper aims to collect and organize the fundamental principles of the Kalman filter and its derivative algorithms (including EKF, UKF, and EnKF), compare their advantages and limitations, and provide examples of their applications in data assimilation.