CSI Amplitude Fingerprinting-Based NB-IoT Indoor Localization

Abstract

With the proliferation of mobile devices, indoor fingerprinting-based localization has caught considerable interest on account of its high precision. Meanwhile, channel state information (CSI), as a promising positioning characteristic, has been gradually adopted as an enhanced channel metric in indoor positioning schemes. In this paper, we propose a CSI amplitude fingerprinting-based localization algorithm in Narrowband Internet of Things system, in which we optimize a centroid algorithm based on CSI propagation model. In particular, in the fingerprint matching, we utilize the method of multidimensional scaling (MDS) analysis to calculate the Euclidean distance and time-reversal resonating strength between the target point and the reference points and then employ the K-nearest neighbor (KNN) algorithm for location estimation. By conjugate gradient method, moreover, we optimize the localization error of triangular centroid algorithm and combine the positioning result with MDS and KNN's estimated position to get the final estimated position. Experiment results show that compared to some existing localization methods, our proposed algorithm can effectively reduce positioning error.