To address the navigation accuracy challenges of low-cost unmanned vehicles in complex motion environments, this paper proposes an integrated navigation filtering algorithm based on multi-modal motion characteristic decomposition. The method combines Kalman Filter and Cubature Kalman Filter dynamically selecting the optimal filtering strategy according to the motion characteristics of the vehicle. In low-dynamic environments, the Kalman Filter is used to improve computational efficiency, while in medium-dynamic environments, the Cubature Kalman Filter is applied to enhance nonlinear state estimation capabilities. The proposed method is validated through simulations using the Precise Strapdown Inertial Navigation System toolbox, analyzing UAV and UGV trajectories. Experimental results show that compared to traditional filtering methods, the proposed algorithm reduces position estimation errors by 25% in UAV scenarios and improves computational efficiency by 50% in UGV scenarios.