The　extended　Kalman　filter　(EKF),　as　a　popular　tool　for　optimally　estimating　system　state　from　noisy　measurement,　has　been　used　successfully　in　various　areas　over　the　past　several　decades.　However,　classical　EKF　has　several　limitations　when　applied　to　structural　system　identification;　thus,　researchers　have　proposed　a　number　of　variations　for　this　method.　The　current　study　focuses　on　using　EKF　for　real-time　system　identification　and　damage　detection　in　civil　structures.　An　improved　EKF　approach,　called　moving-window　EKF　(MWEKF),　is　proposed　in　this　paper　after　a　discussion　on　the　problems　associated　with　the　application　of　classical　EKF　in　time-variant　systems.　The　proposed　approach　uses　the　moving-window　technique　to　estimate　several　statistical　properties.　MWEKF　is　more　robust　and　adaptive　in　structural　damage　detection　compared　with　classical　EKF　because　of　the　following　reasons:　(1)　it　is　insensitive　to　the　selection　of　the　initial　state　vector;　(2)　it　exhibits　more　accurate　system　parameter　identification;　and　(3)　it　is　immune　to　the　inaccurate　assumption　of　noise　levels　because　measurement　and　process　noise　levels　are　estimated　in　this　approach.　The　salient　features　of　MWEKF　are　illustrated　through　numerical　simulations　of　time-variant　structural　systems　and　an　experiment　on　a　three-story　steel　shear　building　model.　Results　demonstrate　that　MWEKF　is　a　robust　and　effective　tool　for　system　identification　and　damage　detection　in　civil　structures.　©　2016　Elsevier　Ltd