Effective Methods to Improve the Accuracy of False Light Source Recognition in Visible Light Indoor Positioning

With the development of visible light communication technology, visible light indoor positioning technology has become one of the research hotspots in indoor positioning technology. Visible light indoor positioning uses the light source information in the scene to locate the receiving end. However, the indoor environment is complex, and there are likely to be mirrors, glass and other reflective objects. These objects reflect to generate a pseudo-light source that transmits information similar to the actual light source, affecting the positioning result. Aiming at the recognition of pseudo-light sources, this paper adds classification steps to the algorithm based on chiral light source array, and proposes two methods that can improve the accuracy of identifying true and false light sources: K-Nearest Neighbor (KNN) classification and Weighted K-Nearest Neighbor (WKNN) classification and correction. Based on the chiral detection algorithm of vector product, we compare the success rates of the improved methods with the algorithm without the improved methods. The simulation results show that the accuracy of false detection can be further improved by adding the improved method, so as to improve the positioning accuracy.