方法对比
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| 分层卡尔曼滤波器× | 卡尔曼滤波器× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1994 | 1960 |
| 提出者≠ | Chou, Willsky & Benveniste | Rudolf E. Kalman |
| 类型≠ | recursive Bayesian state estimator | recursive Bayesian filter |
| 开创性文献≠ | Chou, K. C., Willsky, A. S., & Benveniste, A. (1994). Multiscale recursive estimation, data fusion, and regularization. IEEE Transactions on Automatic Control, 39(3), 464–478. DOI ↗ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| 别名 | multi-scale Kalman filter, multilevel Kalman filter, hierarchical state-space filter, HKF | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| 相关≠ | 4 | 5 |
| 摘要≠ | The Hierarchical Kalman Filter (HKF) extends the classic Kalman filter to systems with multiple levels or scales of state representation. It applies Kalman recursions at each level of a hierarchy — from coarse to fine resolution or from global to local subsystems — and passes information across levels via upward and downward sweeps, producing optimal linear state estimates throughout a structured state-space. | 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. |
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