Abstract
Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields,for instance,smart healthcare,emergency rescue,soldier positioning et al.The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors,disturbed local magnetic field,and complex motion modes of the pedestrian.This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE)frame-work,which can be applied for long-term navigation tasks.A Bi-directional Long Short-Term Memory(Bi-LSTM)based quasi-static magnetic field(QSMF)detection algorithm is developed for extracting useful magnetic observation for heading calibration,and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period.In addition,a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks,and enhanced by the magnetic and trajectory features assisted loop detection algo-rithm.Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algo-rithms,and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min,respectively.
维普
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