基于双线性插值与K-means聚类算法结合的位置指纹定位改进算法

Improved location fingerprint position algorithm based on the combination of bilinear interpolation and K-means clustering algorithm

为了提升可见光通信(VLC)室内环境下的定位精度,提出一种基于双线性插值与K-means聚类算法结合的位置指纹定位改进算法。该算法首先建立初始指纹,然后对待定位点所在聚类域中的指纹进行插值计算,最后将插值后的部分区域指纹作为参考指纹库存,选择一种匹配算法实现最终定位。在5 m×5 m×3 m的室内房间建立VLC系统模型,仿真分析了指纹密集度对定位精度的影响以及改进算法的定位精度对比情况。仿真结果表明:随着指纹库密集度的降低,定位精度随之提高;改进算法使用插值指纹库与比使用初始指纹库的定位精度提高了21.5%,同时大大降低了计算复杂度。

In order to improve the positioning accuracy in the indoor environment of visible light communication(VLC), an improved location fingerprint position algorithm based on the combination of bilinear interpolation and K-means clustering algorithm is proposed. The algorithm first establishes the initial fingerprint, then interpolates the fingerprint in the cluster domain where the location point is located, and finally selects a matching algorithm to achieve the final location by taking the partial regional fingerprint after interpolation as the reference fingerprint inventory. The VLC system model is established in the indoor room of 5 m ×5 m ×3 m, and the influence of fingerprint density on positioning accuracy and the comparison of positioning accuracy of the improved algorithm are simulated and analyzed. The simulation results show that the positioning accuracy increases with the decrease of the intensity. The positioning accuracy of the improved algorithm using the interpolated fingerprint database is improved by 21.5% compared with that using the initial fingerprint database, and the computational complexity is greatly reduced.