Unmanned aerial vehicle (UAV) swarms have seen broad deployment in search and rescue, environmental monitoring, and surveillance. However, these scenarios frequently take place in environments with restricted Global Navigation Satellite System (GNSS) signals and limited accuracy of onboard inertial navigation systems (INS). To address these challenges, this paper proposes a cooperative navigation and positioning framework based on the Generalized Trust Region Subproblem (GTRS). Programmatic and mathematical optimizations are introduced to accelerate GTRS computation, enabling more efficient and precise solutions. The proposed approach fuses GTRS-derived navigation estimates with INS data to enhance localization accuracy in a global coordinate system. In addition, an incremental micro-step motion strategy integrated with an Improved Particle Swarm Optimization (IPSO) algorithm dynamically updates the UAV swarm’s spatial configuration, further refining navigation accuracy and operational efficiency. Simulation results indicate that the root mean square error (RMSE) of the pro-posed method ranges from 0.05 to 0.7 m, with an average RMSE (ARMSE) of 0.18 m and representing 66.0% and 78.8% improvement in positioning accuracy compared to Node Contribution-CDOP and FSSA methods that fuse ranging, angular information, and INS data. This study highlight the potential of the proposed method for achieving high-precision of autonomous UAV swarm navigation.