Abstract: Traditional strapdown inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation systems often suffers from degraded accuracy in complex environments due to GNSS signal blockages, particularly in the altitude channel where errors diverge rapidly. To address this issue, this paper proposes a multi-source data fusion algorithm based on SINS/GNSS/altimeter integration. Firstly, the error model of the inertial navigation altitude channel is established to analyze its divergence mechanism. Secondly, Kalman filtering is employed to perform initial fusion of GNSS altitude data and altimeter measurements, effectively eliminating random constant errors and residual first-order Markov noise. Subsequently, recursive weighted least squares (RWLS) is applied to optimize the secondary fusion of the preliminary results with barometric altimeter data. Finally, during GNSS signal outages, weighted smoothing ensures rapid and stable altitude estimation. Simulation results demonstrate that under GNSS signal blockages, the proposed algorithm reduces altitude errors by over 60% compared to single-altimeter solutions, achieving performance close to that of uninterrupted SINS/GNSS integration. In unobstructed environments, the algorithm reduces altitude errors by more than 17% compared to traditional SINS/GNSS systems. The proposed approach effectively suppresses altitude channel divergence in inertial navigation and significantly enhances the adaptability and precision of integrated navigation systems in complex scenarios.