Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамический фильтр частиц× | Фильтр Калмана× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1993 | 1960 |
| Автор метода≠ | Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001) | Rudolf E. Kalman |
| Тип≠ | Sequential Bayesian state estimation | recursive Bayesian filter |
| Основополагающий источник≠ | Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461 | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| Другие названия | dynamic sequential Monte Carlo, dynamic SMC, bootstrap particle filter, dynamic SIR filter | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| Связанные≠ | 4 | 5 |
| Сводка≠ | A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observations arrive, particle weights are updated via the likelihood and the population is resampled, keeping the representation concentrated on the most probable state regions in a fully nonlinear and non-Gaussian setting. | 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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