Research on WiFi Location Fingerprint Positioning Algorithm Based on DPC-FCM Clustering

Abstract

Aiming at the cost factors and difficulty of wireless positioning technology deployment, as well as the mismatch of similar fingerprints in RSS (received signal strength) indoor fingerprint positioning, the WiFi location fingerprint positioning algorithm based on DPC-FCM clustering is adopted. In this method, the fuzzy C -means (FCM) clustering is used instead of the traditional hard clustering algorithm, which can not only reasonably estimate the RSS feature of the cluster center, avoiding the error of the hard clustering algorithm, but also increase the difference between the reference points. Meanwhile, it can also reduce the complexity of feature matching. Aiming at the problem that the FCM algorithm is easily affected by the initial value and tends to converge to the local extreme value, the density peaks clustering (DPC) algorithm that can accurately characterize the initial center of the cluster is used to make up for the shortcomings of the FCM algorithm and improve the positioning accuracy. Experimental test results show that the positioning algorithm based on DPC-FCM clustering is more accurate than the positioning algorithm based on traditional clustering.