Joint　channel　estimation　and　data　detection　are　always　challenging　problems　for　high　mobility　multiple-input　multiple-output　orthogonal　frequency　division　multiplexing　systems.　Existing　iterative　schemes　focus　on　the　performance　improvement　from　the　correct　ones,　ignoring　error　propagation　from　the　incorrect　ones.　To　deal　with　this　problem,　we　propose　a　new　iterative　channel　estimation　and　detection　scheme　that　reduces　the　unexpected　effects　of　both　detection　errors　and　channel　estimation　errors.　The　interference　in　channel　estimation　induced　by　detection　errors　is　analyzed　and　transformed　as　part　of　the　noise,　which　is　filtered　out　by　Kalman　estimator　using　the　derived　covariance　of　both　the　channel　and　data　errors　in　detection.　Besides,　to　minimize　the　detection　error　caused　by　channel　estimation　errors,　we　propose　a　new　detection　algorithm　with　an　optimized　weight,　which　is　obtained　by　the　error　covariance　of　the　estimated　channels　and　the　detected　data.　Furthermore,　matrix　calculations　and　parameter　approximations　are　performed　to　reduce　computational　complexity.　Extensive　simulation　results　are　also　presented　to　demonstrate　the　significant　performance　improvement　in　joint　channel　estimation　and　data　detection　with　the　proposed　iterative　schemes.