RT Journal Article SR Electronic T1 Overbounding the effect of uncertain Gauss-Markov noise in Kalman filtering JF NAVIGATION: Journal of the Institute of Navigation JO NAVIGATION FD Institute of Navigation SP 259 OP 276 DO 10.1002/navi.419 VO 68 IS 2 A1 Langel, Steven A1 Crespillo, Omar GarcĂ­a A1 Joerger, Mathieu YR 2021 UL https://navi.ion.org/content/68/2/259.abstract AB Prior work established a model for uncertain Gauss-Markov (GM) noise that is guaranteed to produce a Kalman filter (KF) covariance matrix that overbounds the estimate error distribution. The derivation was conducted for the continuous-time KF when the GM time constants are only known to reside within specified intervals. This paper first provides a more accessible derivation of the continuous-time result and determines the minimum initial variance of the model. This leads to a new, non-stationary model for uncertain GM noise that we prove yields an overbounding estimate error covariance matrix for both sampled-data and discrete-time systems. The new model is evaluated using covariance analysis for a one-dimensional estimation problem and for an example application in Advanced Receiver Autonomous Integrity Monitoring (ARAIM).