Robust Kalman Filter Based on a Generalized Maximum-Likelihood-Type Estimator

By Mital A. Gandhi, Lamine Mili. IEEE Transactions on Signal Processing, 2010

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Nonlinear Regression Huber–Kalman Filtering and Fixed-Interval Smoothing (2015, methodology): “Several variants of the Huber-based Kalman (and extendedKalman) filters have been applied to awide range of problems, including underwater vehicle navigation [6], speech signal processing [7], power system state estimation [8], radar tracking [9], spacecraft rendezvous [10], spacecraft attitude esti”
Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts (2012, methodology): “IEEE SIGNAL PROCESSING MAGAZINE [71] JULY 2012”
Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion (2014, background): “However, it breaks down in the presence of outliers (Gandhi and Mili 2010).”
A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation (2017, methodology): “5 leads to high statistical efficiency under Gaussian noise [14].”
A Novel Robust Student's t-Based Kalman Filter (2017, positive): “To solve effectively and robustly the filtering problem of linear systems with both heavy-tailed process and measurement noises, a Huber-based Kalman filter (HKF) has been proposed by minimising a combined l1 and l2 norm [13]–[17].”