Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Фільтр Калмана× | Розширений фільтр Калмана× | |
|---|---|---|
| Галузь≠ | Баєсові методи | Теорія керування |
| Родина≠ | Bayesian methods | Machine learning |
| Рік появи≠ | 1960 | 1961 |
| Автор методу≠ | Rudolf E. Kalman | Richard S. Bucy |
| Тип≠ | recursive Bayesian filter | algorithm |
| Основоположне джерело≠ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ | Bucy, R. S. (1961). A linear approximation to the solution of nonlinear filtering equations. Technical Report No. 32-486, Jet Propulsion Laboratory. link ↗ |
| Інші назви≠ | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter | EKF, Nonlinear Kalman Filter |
| Пов'язані≠ | 5 | 2 |
| Підсумок≠ | The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time. | The Extended Kalman Filter (EKF) is the nonlinear generalization of the Kalman Filter, extending the linear state estimation algorithm to nonlinear systems through local linearization. Developed by Bucy in the early 1960s, the EKF has become the workhorse for state estimation in nonlinear systems across robotics, aerospace, and navigation, enabling real-time processing of noisy measurements from nonlinear sensors and dynamics. |
| ScholarGateНабір даних ↗ |
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