In　this　paper,　a　resilient　adaptive　covariance　Kalman　filter　is　developed　for　state　estimation　under　false　data　injection　attack　(FDIA)　during　the　process　of　measurements　transmission.　The　extreme　measurement　deviation　caused　by　unknown　injection　vectors　is　clipped　by　an　adaptive　saturation　function,　and　an　adaptive　noise　covariance　matrix　triggered　by　prediction　residual　is　constructed　to　enhance　the　estimation　performance　and　stability　of　the　filtering　error　system　under　FDIA.　To　analyze　the　asymptotic　convergence　of　the　algorithm,　the　error　expression　is　constructed　to　analyze　the　upper　limit　of　prediction　error.　Finally,　a　simulation　experiment　on　an　inverted　pendulum　car　verifies　the　stability　and　effectiveness　of　the　proposed　method　in　reducing　the　impact　of　unknown　attack　vectors.