基于粒子滤波和地图匹配的融合室内定位

为提高室内定位的精确性与合理性，该文提出使用粒子滤波融合WiFi指纹定位和行人航位推算，应用室内地图对定位结果进行匹配与矫正。地图匹配中，首先通过室内地图约束粒子的不恰当转移来解决粒子的穿墙问题，然后采用基于回退的穿墙矫正算法对行走轨迹中的穿墙现象进行矫正。仿真实验中，经过粒子滤波融合后估计的行走轨迹更加接近真实轨迹，优于WiFi指纹算法和行人航位推算算法估计的轨迹，而经过地图匹配与矫正后，定位精度和合理性得到进一步提高。

Fused Indoor Localization Based on Particle Filtering and Map Matching

In order to improve the accuracy of localization, the paper applies particle filtering to fuse WiFi fingerprinting and pedestrian dead reckoning (PDR). The map matching algorithm. The map matching algorithm first makes use of the indoor map to constrain the improper transitions of particles to reduce wall-crossing problems of them and then corrects wall-crossing problems of trajectories with a trace-back method. Evaluations show that the trajectories of the fused solution are closer to the real trajectories than WiFi fingerprinting and PDR, and after map matching the trajectories are more accurate and reasonable.