传统室内定位中聚类算法被动依赖定位环境中接入点(Acess Point,AP)数量,导致定位效率低、误差大,室内位置指纹定位研究中AP布局是影响定位精度的关键性因素.因此,采用Intel芯片的嵌入式微系统和美国SignalHound生产的SA44B型测量接收机共同组成传感器网络,根据电波路径损耗建立室内定位的目标函数,采用单纯形法和模拟退火算法融合算法对目标函数进行优化,从而达到最合理的AP室内位置布局,而后改进K-means聚类算法将优化后的AP位置坐标作为初始聚类中心,来提高系统的定位效率和精确度.实验结果表明,与传统K-means算法相比,经过AP位置最优化后的聚类定位算法精度提高了13.8％.%The traditional clustering algorithm passively depends on the number of Access Points(AP) deployed on indoor positioning environment,which leads to low efficiency and high positioning error.The layout of AP is a key factor which affects the positioning accuracy of indoor location fingerprint positioning.So a sensor network is built in this paper,which consists of the Intel chips embedded micro-system and the SA44B measuring receivers produced by Signal Hound US.Firstly,the objective function of indoor positioning is established on the basis of the wave path loss theory.Next,the simulated annealing algorithm and the simplex fusion algorithm are used to optimize the objective function,and then the most reasonable layout of AP indoor location is achieved.Finally,the optimized AP position coordinates as the initial cluster centers that are modified by the K-means clustering algorithm,to improve the positioning efficiency and the precision of the system.The traditional K-means algorithm is used as the comparison object in the paper.The experimental results show that the precision of the clustering localization algorithm after the AP location optimization is improved by 13.8 ％.
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