Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

With the rapid development of WLAN (Wireless Local Area Network) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation. In this paper, it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques, it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning, clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment, the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation, as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.