A Dual-Adaptive Sage-Husa Kalman Filter with Model Matching Check for GNSS/INS-Integrated Navigation.

GNSS/INS integrated navigation combines the long-term stability of satellite positioning with the high-frequency continuity of inertial navigation, and has been widely adopted in vehicle navigation, unmanned systems, and intelligent transportation systems. However, in complex urban environments, GNSS measurement noise exhibits significant time-varying characteristics, while high-dynamic carrier motions may cause mismatches between the dynamic model and the actual motion state. These two factors jointly degrade the performance of conventional Sage-Husa (cSage-Husa) filters, resulting in distorted measurement noise estimation and over-adaptation. To address this issue, this paper proposes a dual-adaptive Sage-Husa filtering algorithm considering model matching. The proposed algorithm integrates a forgetting-factor-based global recursive estimation mechanism with an adaptive sliding window strategy. Furthermore, a model matching test and a measurement noise covariance upper-bound constraint are introduced to distinguish model mismatch errors from normal measurement noise variations, thereby improving filtering stability and noise estimation reliability. Experimental results demonstrate that, under initial high-dynamic motions and instantaneous GNSS interference conditions, the proposed algorithm reduces the up-channel RMS error by 8.5% compared with cSage-Husa. Under sustained GNSS degradation conditions, the north RMS error is reduced by 8.7% and 2.5% compared with EKF and cSage-Husa, respectively, while the up-channel RMS error is reduced by 15% compared with cSage-Husa and maintains comparable accuracy to EKF. The proposed algorithm effectively mitigates over-adaptation and convergence degradation caused by model mismatch, achieving a favorable balance between time-varying noise tracking capability and filtering stability for urban canyon and high-dynamic vehicle positioning.