Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Filtro de Partículas (Monte Carlo Sequencial)× | Filtro de Kalman× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1993 | 1960 |
| Autor original≠ | Gordon, Salmond & Smith | Rudolf E. Kalman |
| Tipo≠ | Sequential Monte Carlo estimator | recursive Bayesian filter |
| Fonte seminal≠ | 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 ↗ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| Outros nomes≠ | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | 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. | 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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