Unscented Kalman Filter-based Multi-IMU Combination for UAV GPS Anomaly Navigation System

In the current unmanned aerial vehicle (UAV) technology, accurate positioning and navigation systems are very important to ensure flight safety and perform precise tasks. Especially when the GPS signal is blocked or unavailable, the traditional navigation system combined with a single micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) often shows significant performance degradation. This paper proposes a multi-IMU combination technology based on unscented Kalman filtering (UKF) to optimize the navigation system of UA V s in the environment of GPS signal failure. By comparing the performance of a single IMU system with that of a system integrated with three IMU s in the simulation environment, and using the NaveGo framework to simulate the system, the research methods include simulating the two system configurations in the scenarios where GPS signals are shielded, to evaluate their position tracking ability and error distribution. The results show that the errors in the upward velocity estimation of each system are relatively stable, mainly within 0.5 m/s. This remarkable performance improvement proves the effectiveness of the multi-IMU system in improving error distribution and enhancing the robustness of the navigation system. The value of this research lies in that it not only improves the navigation accuracy of UAVs when the GPS signal fails but also provides strong technical support for the design of advanced navigation systems for UAV s in the future.