Outlier-Insensitive Kalman Filtering: Theory and Applications
This work addresses outlier sensitivity in Kalman filtering for applications like robotics and signal processing, offering an incremental improvement over existing outlier detection methods.
The paper tackled the problem of state estimation in dynamical systems where outliers degrade Kalman filter performance, proposing a parameter-free algorithm that models outliers as normal processes with unknown variance and uses online estimation to achieve robust filtering with competitive results in simulations and field experiments.
State estimation of dynamical systems from noisy observations is a fundamental task in many applications. It is commonly addressed using the linear Kalman filter (KF), whose performance can significantly degrade in the presence of outliers in the observations, due to the sensitivity of its convex quadratic objective function. To mitigate such behavior, outlier detection algorithms can be applied. In this work, we propose a parameter-free algorithm which mitigates the harmful effect of outliers while requiring only a short iterative process of the standard update step of the KF. To that end, we model each potential outlier as a normal process with unknown variance and apply online estimation through either expectation maximization or alternating maximization algorithms. Simulations and field experiment evaluations demonstrate competitive performance of our method, showcasing its robustness to outliers in filtering scenarios compared to alternative algorithms.