In　view　of　that　there　exist　some　defects　when　the　extended　Kalman　filter　(EKF)　is　applied　in　the　nonlinear　state-estimation,　the　square-root　unscented　Kalman　filter　(SRUKF),　as　a　new　nonlinear　filtering　method,　is　introduced　to　instead　of　the　EKF　for　the　state-estimation　of　the　vehicle　integrated　GPS/DR　navigation　system.　Compared　with　the　EKF,　the　SRUKF　not　only　improves　the　location　precision　and　algorithmic　stability　greatly,　but　also　avoids　the　calculating　burden　of　Jacobian　matrices.　This　data　fusion　algorithm　based　on　the　SRUKF　is　easy　to　implement,　and　meets　the　requirements　of　low-cost　and　high　precision.　In　order　to　test　the　validity　of　the　SRUKF,　the　two　methods　are　used　to　estimate　states　of　the　vehicle　integrated　GPS/DR　navigation　systems.　The　results　of　simulation　show　that　the　SRUKF　is　superior　to　the　EKF　and　is　a　more　ideal　nonlinear　filtering　method　for　the　vehicle　integrated　GPS/DR　navigation.