方法对比

	鲁棒卡尔曼滤波器 (Robust Kalman Filter)	卡尔曼滤波器
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	1977	1960
提出者	Derived from Kalman (1960); robust extensions developed by Masreliez, Martin, and others from the 1970s onward	Rudolf E. Kalman
类型	Sequential Bayesian state estimator with robustified update step	recursive Bayesian filter
开创性文献	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.
别名	RKF, heavy-tailed Kalman filter, outlier-robust Kalman filter, robust state estimation	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
相关	5	5
摘要	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.