一种融合地图与传感器信息的室内地图匹配新算法

摘要

This paper proposes a new map matching algorithm based on particle filter,which fuses map and sensor information. Firstly,the nonlinear indoor positioning system is described by state space model. Then, the recursive Bayesian filtering is implemented through the parameterized Monte Carlo method,and the in-door geographic data,sensors,wireless positioning information are fused to modify the weights of the parti-cles and the observed value. The experiment shows that the proposed map matching technology can effec-tively solve the user experience problems caused by wrong positioning result. Also,this algorithm improves the indoor positioning accuracy.%基于粒子滤波技术，提出了融合地图信息与传感器信息的室内地图匹配算法，对于在室内定位中由状态空间模型描述的非线性系统，通过非参数化的蒙特卡洛( Monte Carlo)模拟方法来实现递推贝叶斯滤波，将室内地理信息数据、传感器信息、无线定位信息融入到粒子的权重值中，对观测值进行不断修正。实验证明，所提出的基于粒子滤波的地图匹配技术有效解决了由于无线定位结果穿墙、错定至隔壁房间而造成的用户体验差等问题，同时对室内定位结果进行了修正，提高了室内定位精度。