Outlier-Robust Iterative Extended Kalman Filtering
Yangtianze Tao, Stephen S.‐T. Yau
Abstract
In this paper, we develop OR-IEKF which is a novel outlier-robust iterative extended Kalman filtering (IEKF) framework based on nonlinear regression formulation of update step. A new Kalman-type update step with reweighted prediction covariance and reweighted observation noise covariance are produced under the OR-IEKF framework, which could cut off the large outliers in observations causing by unknown outlier noises. By using various robust cost functions to solve such special nonlinear regression problems, we derive three algorithms. The performances of these new filters are evaluated in a nonlinear system simulation study.
Topics & Concepts
OutlierKalman filterCovarianceExtended Kalman filterComputer scienceAnomaly detectionAlgorithmNoise (video)Covariance matrixNonlinear systemIterative methodRobustness (evolution)Artificial intelligencePattern recognition (psychology)MathematicsStatisticsImage (mathematics)Quantum mechanicsPhysicsBiochemistryGeneChemistryTarget Tracking and Data Fusion in Sensor NetworksStructural Health Monitoring TechniquesControl Systems and Identification