方法对比
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| 卡尔曼滤波器× | 扩展卡尔曼滤波器× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 控制理论 |
| 方法族≠ | Bayesian methods | Machine learning |
| 起源年份≠ | 1960 | 1961 |
| 提出者≠ | Rudolf E. Kalman | Richard S. Bucy |
| 类型≠ | recursive Bayesian filter | algorithm |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter | EKF, Nonlinear Kalman Filter |
| 相关≠ | 5 | 2 |
| 摘要≠ | 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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