基于双阶段位置修正的室内定位算法

WLAN指纹定位技术已经成为室内定位领域的研究热点,但空间环境变化易导致传统定位算法精度降低。针对此问题,提出基于双阶段位置修正的室内定位算法。分析空气介质电导率变化对RSSI的影响,以传统算法的定位结果作为初始位置,首先利用K邻近法(KNN)构建初始位置指纹映射;在此基础上,利用多维标度法(MDS)计算离线、在线阶段的用户间相对位置修正值;最后,利用双阶段位置修正值对初始位置进行优化,得出最终目标位置。实验结果表明,该算法能够有效应对环境变化,修正定位结果,传统算法经其优化后平均误差均有10%以上的降低。

Indoor Location Algorithm Based on Two-stage Position Correction

WLAN fingerprint location technology has become a hot topic in the field of indoor location, but the traditional location algorithm are particularly susceptible to spatial environment changes which will reduce the accuracy. To solve this problem, this paper proposes the indoor location algorithm based on two-stage position correction. This paper analyzes the influence of the conductivity of indoor air on the RSSI. On the basis of the traditional algorithms’ locating results as initial location position, this algorithm uses K-Nearest Neighbor algorithm (KNN) to construct all users’ initial location position and fingerprint mapping. On this foundation, off-line and on-line stages position correction between users by multidimensional scaling (MDS) is calculated. At the last, the initial location position is optimized with two-stage position correction and the end localization result of the target is obtained. The experimental results show that the algorithm can deal with the dynamic changes of the environment effectively and correct the location results. The average error of the traditional algorithm is reduced by more than 10% after optimization of this algorithm.