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

作　　者：Tao Feng Yu Liu Yue Yu Liang Chen Ruizhi Chen 

机构地区：[1]School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [2]Department of Land Surveying and Geo-Informatics,The Hong Kong Polytechnic University,Hong Kong,999077,China [3]Chongqing Key Laboratory of Autonomous Navigation and Microsystem,Chongqing University of Post and Telecommunications,Chongqing,400065,China [4]State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing(LIESMARS),Wuhan University,Wuhan,430000,China

出　　处：《Defence Technology（防务技术）》2024年第10期78-90,共13页Defence Technology

摘　　要：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) 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 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 algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and 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 

分 类 号：E91[军事]