Outlier-Robust Iterative Extended Kalman Filtering

In this letter, 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.

Automated summary

In this paper, we propose a novel outlier-robust iterative extended Kalman filtering (OR-IEKF) framework based on nonlinear regression formulation of update step. The OR-IEKF framework introduces a new Kalman-type update step with reweighted prediction covariance and reweighted observation noise covariance, which can eliminate large outliers caused by unknown outlier noises. By employing robust cost functions, three algorithms are derived to solve the special nonlinear regression problems. The performances of these new filters are evaluated in a simulation study of a nonlinear system.