Nowadays,pedestrian navigation technology is playing an increasingly important role in supermarket shopping,fire rescue and field exploration,and the pedestrian navigation and positioning without global navigation satellite system (GNSS) has become an indispensable link.The self-contained sensors are used as a hardware platform for research on pedestrian autonomous navigation in non-GNSS environment.A zero-speed comprehensive discriminant algorithm based on the "2 + 2" hierarchical model is studied to improve the accuracy and reliability of zero velocity update (ZUPT).Kalman filter algorithm based on ZUPT designed for inertial navigation system is used to effectively suppress the sensor error divergence.To solve the problem of pedestrian long-term heading divergence,the magnetic heading error self-observation algorithm (MHESO) for pedestrian initial static state and the ZUPT heading error self-observation algorithm (ZHESO) for pedestrian movement are studied.In addition,a geomagnetic matching (GM) algorithm based on multi-layer constraint and K-nearest neighbor algorithm is proposed,and the fusion of ZUPT_HESO-pedestrian navigation algorithm and geomagnetic matching algorithm is realized,which improves the accuracy and reliability of pedestrian navigation.The actual data test proves that the proposed pedestrian navigation algorithm based on ZUPT_HESO_GM effectively improves the positioning accuracy by more than 79％.%目前行人导航技术正发挥着越来越重要的作用,而无全球导航卫星系统(GNSS)环境下的行人导航定位成为其不可或缺的环节.以自包含传感器为硬件平台,针对无GNSS环境下的行人自主导航定位展开研究,提出一种基于“2+2”分级模式的零速判别方法,并设计一种惯性导航系统的零速修正卡尔曼滤波算法,有效提高同一参数阈值下零速判别的准确性与可靠性、抑制传感器误差发散;研究行人初始静态下磁航向误差观测算法及行人运动状态下的零速航向误差自观测算法,解决了行人长时间行走航向发散问题;提出基于多层约束和K近邻算法的地磁匹配算法,并实现基于零速修正/航向误差自观测的行人导航算法与地磁匹配算法的融合,提高了行人导航定位精度与可靠性.实际数据测试验证,所提基于零速修正/航向误差自观测/地磁匹配的行人导航算法可有效提高定位精度79％以上.
展开▼
