Sensor data fusion optimization in UAV integrated navigation based on matrix factorization

Although significant progress has been made in multi-source information fusion for Unmanned Aerial Vehicle integrated navigation systems, there are still obvious shortcomings in real-time redundant compression of high-dimensional heterogeneous observation data and suppression of abnormal interference in complex dynamic environments. Most existing UAV fusion methods lack robustness in complex scenarios due to either limited temporal modeling or non-adaptive regularization. To address this issue, this study proposes an integrated navigation state estimation algorithm for Unmanned Aerial Vehicle sensor data fusion optimization. The hybrid architecture integrates RPCA-based noise separation, transformer-enhanced temporal modeling, and adaptive λ regularization achieved through shallow networks. Experimental results show that when switching between multiple scenarios and the abnormal observation ratio increases to 40% or higher, the system output error only increases by 0.044 m, significantly lower than the comparison algorithms with 0.055 m or more. In addition, the algorithm maintains an error upper bound below 0.1 m under low integrity conditions. The proposed optimization algorithm significantly improves the environmental adaptability and robustness of Unmanned Aerial Vehicle integrated navigation systems and provides theoretical and methodological support for autonomous operation of multi-sensor navigation systems in complex environments.