室内可见光指纹定位系统中改进的加权K最近邻算法

Improved Weighted K Nearest Neighbor Algorithm for Indoor Visible Light Fingerprint Positioning System

针对室内可见光指纹定位系统中加权K最近邻(WKNN)算法用欧氏距离不能有效表示各测量点间实际距离的问题,提出了一种基于加权欧氏距离度量的改进WKNN算法。该算法根据接收信号强度随实际距离变化的衰减特性,为不同的信号强度差值分配不同的加权系数。仿真结果表明,在相同的环境条件下,相比基于欧氏距离和曼哈顿距离的WKNN算法,改进算法的平均定位误差分别降低了37.5%和34.3%。

Aiming at the problem that the Euclidean distance in the weighted K nearest neighbor(WKNN)algorithm can not effectively represent the actual distance relationship between measurement points in the indoor visible light fingerprint positioning system,an improved WKNN algorithm based on weighted Euclidean distance measurement is proposed in this paper.The algorithm assigns different weighting coefficients to different signal strength differences according to the attenuation characteristics of the received signal strength varying with the actual distance.The simulation results show that under the same environmental conditions,compared with the WKNN algorithm using European distance measurement and Manhattan distance measurement,the average positioning error of the improved algorithm is reduced by 37.5%and 34.3%,respectively.