This paper is devoted to development of analytical approach to the Kalman filter study. This approach is based upon making use of Riccati equation solution for a class of dynamic systems considered, as well as Kalman and Wiener filters transition matrices. An example of a moving object state estimation shows advantages of the method proposed, the main of which are substantial reduction of computational expenses on modelling of estimation algorithm and a possibility of analytical study of estimation results. A case of correlated measurement noise is also considered. ; Optimal and suboptimal filters are applied to this case, and expressions for filtering errors are derived. The results obtained can be used to determine a possibility of using suboptimal instead of optimal filters, and to reduce computational expenses.
Jul 28, 2020
Jul 7, 2020
|Analytical study of the Kalman filter for stationary dynamic systems||Jul 28, 2020|
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