Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Фільтр Калмана× | Фільтр частинок (послідовний Монте-Карло)× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 1960 | 1993 |
| Автор методу≠ | Rudolf E. Kalman | Gordon, Salmond & Smith |
| Тип≠ | recursive Bayesian filter | Sequential Monte Carlo estimator |
| Основоположне джерело≠ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. 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 ↗ |
| Інші назви≠ | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| Пов'язані≠ | 5 | 4 |
| 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 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. |
| ScholarGateНабір даних ↗ |
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