D-SVM Fusion Clustering Algorithm Based on Indoor Location

Traditional fingerprint orientation clustering algorithms often use k means clustering algorithm, but as a result of fingerprint and objective factors of volatile characteristics over time, k-means cannot adapt to change at any time in fingerprint, and cannot be generated adaptive clustering cluster number, cause the matching accuracy is not high. This paper adopts a based on support vector machine (SVM) and DBSCAN clustering algorithm, can generate continuously adapt to changing the optimal hyperplane fingerprint model, solved the fingerprint fluctuating lead to the problem of matching result is bad, and can be automatically generated in the process of matching classification number of clusters, based on statistical density characteristics of DBSCAN selection matching probability model, to improve the positioning of the matching accuracy, reduced the amount of time matching positioning, positioning accuracy can be up to 2.04 m in the range of 57%, relative k-means 6.1 m increased by 52.3%, improve the positioning accuracy.