An Improved WKNN Indoor Fingerprinting Positioning Algorithm Based on Adaptive Hierarchical Clustering

Aiming at the dependence of the traditional indoor clustering positioning accuracy on the initial center and clustering number selection, an improved WKNN indoor fingerprint localization algorithm based on adaptive H clustering algorithm is proposed in this thesis. Specifically, an adaptive hierarchical clustering combined with positioning environment and fingerprint information without initial clustering center is introduced. At the same time, a RSSI information compensation method based on cosine similarity is proposed aiming at the problem of RSSI information packet loss for test nodes in complicated indoor location environment, with the result of positioning error decrease at test node by using cosine similarity between test nodes and fingerprint points to approximately compensate the missing RSSI information. The experimental results indicate that the proposed adaptive hierarchical clustering algorithm can divide the experimental area adaptively according to fingerprint information, meanwhile the proposed fingerprint information compensation method can decrease the positioning error of the test node with incomplete information, by which the average positioning error in the experimental environment is decreased to 0.78 m compared with other indoor positioning algorithms.