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| Robuster Partikelfilter× | Kalman Filter× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1998-2004 | 1960 |
| Urheber≠ | Hurzeler & Kunsch; Ristic, Arulampalam & Gordon | Rudolf E. Kalman |
| Typ≠ | Sequential Bayesian estimation | recursive Bayesian filter |
| Wegweisende Quelle≠ | Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318 | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| Aliasnamen | RPF, robust sequential Monte Carlo, outlier-robust particle filter, heavy-tailed particle filter | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | The Robust Particle Filter is a sequential Monte Carlo method that tracks hidden states in nonlinear, non-Gaussian systems while remaining resistant to outliers and model misspecification. It replaces the standard Gaussian likelihood with a heavy-tailed or bounded-influence density, so that anomalous observations receive downweighted importance and cannot derail the state estimate. | 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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