Outlier-Insensitive Kalman Filtering: Theory and Applications

arXiv:2309.09505v2 Announce Type: replace-cross
Abstract: 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 …

abstract applications arxiv cs.lg eess.sp filter filtering function linear outlier outliers performance sensitivity state systems theory type