为了解决卫星导航信号被遮挡条件下定位问题，该文提出一种基于惯性鞋载传感器的高精度人员自主定位方法。该方法通过经典的拓展卡尔曼滤波辅助的零速更新(ZUPT-aided EKF)算法解算鞋尖惯性测量数据得到人员的初步运动轨迹，并创新性地提出一种粒子滤波框架下利用建筑物结构先验知识对轨迹进行修正的方法。根据大多数建筑物的结构，将行走平面划分为8个方向，包含4个主方向(走廊朝向)和4个辅助方向。根据粒子偏离8方向的程度按照高斯函数对粒子的权值进行更新，并用剩余重采样的方法避免了粒子的退化。实测数据验证了该文提出的方法，结果表明：该方法比轨迹修正前和传统轨迹修正的方法有更好的精度，在861 m的复杂轨迹下定位误差仅为2.7 m，定位精度优于0.5%；同时该方法有较好的一致性，不同楼层间的行走定位误差保持在2 m内, 可以进行稳定持续地定位。
During GPS outages, the foot-mounted inertial-based sensors are common replacement in pedestrian navigation. The Zero velocity UPdaTe-aided Extended Kalman Filter (ZUPT-aided EKF) is often used to resolve the trajectory of a walking pedestrian with acceleration and angular rate measurements from foot-mounted sensors. However, the trajectory suffers from long-term drifts, which needs to be calibrated. This paper proposes a particle filter based approach for trajectory calibration, which exploits apriori knowledge of building structures to update particle weight. The buildings are supposed to have four domain directions, which is defined by the layout of corridors. The navigation frame is divided by eight directions, including four domain directions and four complementary directions, and the weight is assigned according to the eight directions using a Gaussian function. Finally, several real-scenario experiments are carried out, which can demonstrate that the proposed approach have better accuracy and consistency than the results without calibration or traditional methods, as the proposed approach can reach a location error of 2.7 m in a complex-trajectory walk of 861 m and the accuracy is better than 0.5%; the fact that the location error remains below 2 m in different floors also demonstrates the good consistency of the approach. As a result, the proposed approach can perform stable and continuous positioning.