Comparar métodos

	Filtro de Kalman Robusto	Filtro de Kalman
Campo	Bayesiano	Bayesiano
Familia	Bayesian methods	Bayesian methods
Año de origen	1977	1960
Autor original	Derived from Kalman (1960); robust extensions developed by Masreliez, Martin, and others from the 1970s onward	Rudolf E. Kalman
Tipo	Sequential Bayesian state estimator with robustified update step	recursive Bayesian filter
Fuente seminal	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Alias	RKF, heavy-tailed Kalman filter, outlier-robust Kalman filter, robust state estimation	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Relacionados	5	5
Resumen	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 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.