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A data and physical model dual-driven based trajectory estimator for long-term navigation

Defence Technology（防务技术） 2024年第10期40卷 78-90页

作者： Tao Feng Yu Liu Yue Yu Liang Chen Ruizhi Chen School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong Kong999077China Chongqing Key Laboratory of Autonomous Navigation and Microsystem Chongqing University of Post and TelecommunicationsChongqing400065China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing(LIESMARS)Wuhan UniversityWuhan430000China

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 *** 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 *** paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE)framework,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 *** 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 ***-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms,and final estimated heading and positioning accuracy indexes reaches 5and less than 2 m under the time period of 30 min,respectively.

关键词： Long-term navigation Wearable inertial sensors Bi-LSTM QSMF data and physical model dual-driven

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