在微机电惯性测量单元(micro-electro-mechanical system-inertial measurement unit, MEMS-IMU)人体室内定位技术研究领域中，行人航迹推算法(pedestrian dead reckoning, PDR)具有计算简便、对传感器精度要求低的优点，得到了较为广泛的应用，但传统的PDR 研究通常只针对单一的二维前进行走运动模式，这与人体实际的三维复杂运动模式相距甚远. 该文从人体三维室内定位研究角度出发，通过10 轴MEMS 传感器采集人体运动原始信息，提出了三维复杂运动模式航迹推算法. 首先使用双零点交叉区间内的峰值双轴检测法与俯仰角检测法来检测5 类非行走运动模式，排除其对有效跨步检测的干扰；其次使用相位反转法与气压值突变法区分3 类行走运动模式，提取出不同类型的有效跨步；最后针对每一个有效跨步求解出人体位置的三维坐标值，完成人体三维室内定位. 实验表明，所提出的三维复杂运动模式航迹推算法在人体实际室内运动中，相较传统的峰值检测和零点交叉法PDR，水平定位精度提升了99%，并且高度定位精度可以达到92.4%.
In human indoor navigation research, pedestrian dead reckoning (PDR) involves less calculation with lower accuracy sensor, and therefore becomes popular in micro-electro-mechanical system-inertial measurement unit (MEMS-IMU). However, conventional PDR approaches only consider 2D forward motion mode,which is unrealistic. This paper uses an approach named 3D complex motion mode PDR to describe human’s indoor life. By collecting the original human motion information with a 10-axis MEMS sensor, this paper proposes a 3D complex motion mode PDR algorithm. 2-axis detection of 2-peak within double zero-crossing and pitch detection methods are used to detect 5 kinds of non-walking motion mode to reduce interference of step detection. Phase reversal and pressure mutation methods are used to distinguish 3 kinds of walking motion mode to record every effective step. The 3D coordinates of the human location are then calculated. Experimental results show that, for the actual human indoor 3D and complex motion mode, the proposed algorithm increases the horizontal positioning accuracy by 99% compared with conventional peak detection and zero-crossing detection PDR. Accuracy of height positioning reaches 92.4%.
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