Vertaile menetelmiä
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| Hierarkkinen Kalman-suodin× | Partikkelisuodin (sekventiaalinen Monte Carlo)× | |
|---|---|---|
| Tieteenala | Bayesilainen tilastotiede | Bayesilainen tilastotiede |
| Menetelmäperhe | Bayesian methods | Bayesian methods |
| Syntyvuosi≠ | 1994 | 1993 |
| Kehittäjä≠ | Chou, Willsky & Benveniste | Gordon, Salmond & Smith |
| Tyyppi≠ | recursive Bayesian state estimator | Sequential Monte Carlo estimator |
| Alkuperäislähde≠ | 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 ↗ | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113. DOI ↗ |
| Rinnakkaisnimet≠ | multi-scale Kalman filter, multilevel Kalman filter, hierarchical state-space filter, HKF | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | 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 particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive. |
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