Research on Indoor Visible Light Location Based on Fusion Clustering Algorithm

Abstract

Aiming at the problem of large positioning errors in the boundary area, a new location fingerprint location method based on a fusion clustering algorithm is proposed. This clustering-based method embodies the idea of rough location first and then fine location. Firstly, the edge regions of the received signal strength (RSS) samples which are greatly affected by reflection are divided using the k-medoids algorithm, and then the center part is clustered via density-based spatial clustering of applications with noise (DBSCAN). In the actual location estimation stage, the points to be measured can only be located in one of the classified areas, and combined with the optimal k-nearest neighbor algorithm (WOKNN) to match the location. The results show that the average positioning error of the algorithm is 13 cm in an indoor environment of 5 m × 5 m × 3 m. Compared with the traditional method without clustering, the positioning accuracy of the edge area is increased by 21%, and the overall improvement is 33.8%, which proves that the proposed algorithm effectively improves the efficiency of real-time positioning and indoor positioning accuracy.