  In this paper we investigate the performance of projection-based low-rank
approximations in Kalman filtering. For large-scale gas pipeline networks
structure-preserving model order reduction has turned out to be an advantageous
way to compute accurate solutions with much less computational effort. For
state estimation we propose to combine these low-rank models with Kalman
filtering and show the advantages of this procedure to established low-rank
Kalman filters in terms of efficiency and quality of the estimate.
