PT - JOURNAL ARTICLE AU - Steven Langel AU - Omar GarcĂ­a Crespillo AU - Mathieu Joerger TI - Overbounding the effect of uncertain Gauss-Markov noise in Kalman filtering AID - 10.1002/navi.419 DP - 2021 Jun 20 TA - NAVIGATION: Journal of the Institute of Navigation PG - 259--276 VI - 68 IP - 2 4099 - https://navi.ion.org/content/68/2/259.short 4100 - https://navi.ion.org/content/68/2/259.full SO - NAVIGATION2021 Jun 20; 68 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).