Indoor Visible Light Positioning Method Based on Sparse Fingerprints Using Extended Min-Max and WKNN Algorithms

Indoor visible light positioning (VLP) systems based on received signal strength (RSS) fingerprint can provide high-precision indoor positioning with low complexity. However, laborious offline field measurements are required to collect RSS fingerprints, and the subsequent fingerprint matching process also needs a large computation overhead. In order to address the above problems, in this paper, we propose an indoor VLP method based on sparse fingerprints using extended Min-Max (E-Min-Max) and weighted k-nearest neighbor (WKNN) algorithms. In the proposed VLP method, bicubic interpolation is used during the offline phase to directly estimate the RSS values of virtual reference nodes. In the online phase, a coarse estimate of the position of target node (TN) is firstly obtained by using the E-Min-Max algorithm. Then, based on the coarse estimate of the TN’s position, the WKNN algorithm is used to determine the TN’s accurate position. Simulation results show that compared to the traditional WKNN algorithm, the proposed VLP method has much higher fingerprint matching efficiency while ensuring high positioning accuracy.