Optimal Path Planning Method for IMU System-Level Calibration Based on Improved Dijkstra’s Algorithm

The calibration path of system-level directly affects the incentive effect error term and thus accuracy. Currently, planning paths is predominantly designed based on personal experience, resulting in insufficient for terms, low accuracy, long times. Therefore, this study proposes a optimal method an improved Dijkstra’s algorithm. First, problem was modeled as multi-fork regular root tree model, adaptability algorithm improved. Second, 30-dimensional Kalman filter model calibration. Then, simulation experiments were conducted, results demonstrated that accuracy reached 90% within 330 s. Finally, Micro-Electro-Mechanical system (MEMS) inertial sensor, PA-IMU488B, used experimental verification, compared with discrete results. indicate bias scale factor errors MEMS sensor target 5 min. proposed not dependent high-precision turntable, applicable to sensors different accuracies, decreases time while ensuring