Comparar métodos

	Filtro de Kalman Robusto	Filtro de Partículas (Monte Carlo Sequencial)
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	1977	1993
Autor original	Derived from Kalman (1960); robust extensions developed by Masreliez, Martin, and others from the 1970s onward	Gordon, Salmond & Smith
Tipo	Sequential Bayesian state estimator with robustified update step	Sequential Monte Carlo estimator
Fonte seminal	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.	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.
Outros nomes	RKF, heavy-tailed Kalman filter, outlier-robust Kalman filter, robust state estimation	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Relacionados	5	4
Resumo	The Robust Kalman Filter is an extension of the classical Kalman filter designed to maintain reliable state estimation when observations or process noise depart from the Gaussian assumption — particularly when data contain outliers, heavy-tailed distributions, or gross errors. By replacing or downweighting the standard least-squares update with influence-limited or M-estimation-based corrections, it prevents a single anomalous measurement from distorting the entire 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.