Algorithms for the synthesis of dynamic object control systems significantly use the
concepts of dynamic Kalman filtration. An iterative algorithm for estimating adaptive filters in a
dynamic object control system is presented. For the system of linear equations, the optimal
algorithm for evaluating the Kalman filter and the calculation scheme are given. The Kalman filter
uses measurements a state vector estimate and the corresponding estimation error covariance
matrix. At the same time, an iterative algorithm for calculating the gain of the Kalman filter is proposed. The considered iterative algorithms are based on the available a priori information, in
particular, on the error of the initial data, to select from the entire sequence some approximation
that is sufficiently close to the original solution.
Algorithms for the synthesis of dynamic object control systems significantly use the
concepts of dynamic Kalman filtration. An iterative algorithm for estimating adaptive filters in a
dynamic object control system is presented. For the system of linear equations, the optimal
algorithm for evaluating the Kalman filter and the calculation scheme are given. The Kalman filter
uses measurements a state vector estimate and the corresponding estimation error covariance
matrix. At the same time, an iterative algorithm for calculating the gain of the Kalman filter is proposed. The considered iterative algorithms are based on the available a priori information, in
particular, on the error of the initial data, to select from the entire sequence some approximation
that is sufficiently close to the original solution.
| № | Muallifning F.I.Sh. | Lavozimi | Tashkilot nomi |
|---|---|---|---|
| 1 | Sevinov J.U. | teacher | TSTU |
| 2 | Zaripov O.O. | teacher | TSTU |
| 3 | Zaripova S.O. | teacher | TSTU |
| № | Havola nomi |
|---|---|
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