Various　measurements　are　now　available　for　structural　health　monitoring　(SHM)　due　to　the　fast　development　of　sensory　systems.　Utilization　of　multitype　measurements　including　local　and　global　information　for　SHM　has　typically　outperformed　that　of　solo　type　measurements.　However,　the　limited　number　of　sensors　for　measurements　hampers　the　effectiveness　of　SHM.　Thus,　response　reconstruction　at　the　locations　of　interest　in　which　sensors　are　unavailable　with　limited　measurements　has　drawn　significant　research　attention.　The　Kalman　filter　(KF)　is　a　powerful　tool　to　estimate　optimally　the　unknown　state　vector　of　a　structure　that　has　numerous　applications　in　civil　engineering.　One　main　concern　for　KF　is　that　it　requires　good　estimates　of　the　noise　covariance　information,　which　is　generally　difficult　to　determine.　Therefore,　this　paper　investigates　the　dual-type　responses　reconstruction　by　using　the　moving-window　Kalman　filter　(MWKF)　with　unknown　measurement　noise　covariance　(MNC).　The　weighted　average　of　the　MNC　was　first　evaluated　by　utilizing　the　moving-window　estimation　technique.　Then　the　dual-type　of　measurements　including　strains　and　displacements　were　fused　together　to　reconstruct　the　structural　responses　at　unmeasured　locations.　Numerical　and　experimental　investigations　were　conducted　to　verify　the　effectiveness　and　feasibility　of　the　MWKF　in　dual-type　response　reconstruction.　The　results　indicate　that　the　MNC　can　be　well　estimated　and　the　reconstructed　responses　agree　well　with　the　real　or　measured　responses.　(c)　2019　American　Society　of　Civil　Engineers.