方法对比
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| 稳健粒子滤波器× | 粒子滤波器（序贯蒙特卡洛）× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1998-2004 | 1993 |
| 提出者≠ | Hurzeler & Kunsch; Ristic, Arulampalam & Gordon | Gordon, Salmond & Smith |
| 类型≠ | Sequential Bayesian estimation | Sequential Monte Carlo estimator |
| 开创性文献≠ | Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318 | 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 ↗ |
| 别名≠ | RPF, robust sequential Monte Carlo, outlier-robust particle filter, heavy-tailed particle filter | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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 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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