In order to improve the indoor positioning accuracy and reduce the impact of a reflected light on the positioning performance, based on the analysis of the existing RSS positioning algorithm, an improved positioning algorithm based on K-means clustering is proposed. According to the indoor visible light communication system model and optical power distribution, 10*10 receiving points are uniformly selected on the receiving surface of 5m*5m. Through the collection of the received optical power of each receiving point, the K-means clustering algorithm is used to classify 100 receiving points: the first cluster of points is less affected by a reflected light, and the second cluster of points is greatly affected by a reflected light. The second cluster of receiving points is weighted after classifying, and the simulation results show that the algorithm significantly reduces the room edge positioning error, and the positioning performance of the entire room is improved by 28%.

为了提高室内定位精度，减少反射光对定位性能的影响，在分析现有RSS定位算法的基础上，提出了一种基于K-means聚类的改进定位算法。根据室内可见光通信系统型号和光功率分布，在5m*5m的接收面上统一选择10*10个接收点。通过收集每个接收点的接收光功率，使用K-means聚类算法对100个接收点进行分类：第一组点受反射光影响较小，第二组点受反射光影响较大反射光。第二组接收点在分类后进行加权，