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| 칼만 필터× | 확장 칼만 필터× | |
|---|---|---|
| 분야≠ | 베이지안 | 제어이론 |
| 계열≠ | Bayesian methods | Machine learning |
| 기원 연도≠ | 1960 | 1961 |
| 창시자≠ | Rudolf E. Kalman | Richard S. Bucy |
| 유형≠ | recursive Bayesian filter | algorithm |
| 원전≠ | 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 ↗ |
| 별칭≠ | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter | EKF, Nonlinear Kalman Filter |
| 관련≠ | 5 | 2 |
| 요약≠ | 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. |
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