System-Level Enhanced Pedestrian Autonomous Positioning Method Based on Hierarchical Optimization Under Sparse Reference

To address the issue of positioning accuracy significant degradation in foot kinematics/micro inertial navigation system integrated positioning over long durations, this paper proposes a system-level enhanced pedestrian autonomous positioning (SE-PAP) method based on hierarchical optimization under sparse reference information. The position sequence output of the integrated positioning system is decoupled into the odometer sequence and direction sequence. A SE-PAP model is constructed based on the mechanisms of odometer error and direction error generation. Subsequently, the displacement vector error cost function is established. A hierarchical optimization method is employed to identify and update the SE-PAP model parameters online with minimizing the cost function, thereby enhancing the performance during the autonomous positioning phase. Experimental results show that the proposed method improves positioning performance by up to 65.72% compared with the Traditional Kalman Filter-based pedestrian positioning method and by up to 54.26% compared with the Extended Kalman Filter-based SE-PAP method. The proposed method effectively handles variations in sparse reference information frequency, demonstrating strong adaptability to heterogeneous tester physiologies, environmental conditions, and motion gaits.