For　vehicle　integrated　navigation　systems,　real-time　estimating　states　of　the　dead　reckoning　(DR)　unit　is　much　more　difficult　than　that　of　the　other　measuring　sensors　under　indefinite　noises　and　nonlinear　characteristics.　Compared　with　the　well　known,　extended　Kalman　filter　(EKF),　a　recurrent　neural　network　is　proposed　for　the　solution,　which　not　only　improves　the　location　precision　and　the　adaptive　ability　of　resisting　disturbances,　but　also　avoids　calculating　the　analytic　derivation　and　Jacobian　matrices　of　the　nonlinear　system　model.　To　test　the　performances　of　the　recurrent　neural　network,　these　two　methods　are　used　to　estimate　the　state　of　the　vehicle＇s　DR　navigation　system.　Simulation　results　show　that　the　recurrent　neural　network　is　superior　to　the　EKF　and　is　a　more　ideal　filtering　method　for　vehicle　DR　navigation.