%0 Journal Article
%A LIU Liu
%A PIAO Zhe-yan
%A WANG Kai
%A ZHANG Jia-chi
%A ZHOU Tao
%T The Extraction and Tracking Trajectory of Wireless Channel Tap Clusters Based on Machine Learning
%D 2019
%R 10.13190/j.jbupt.2018-298
%J Journal of Beijing University of Posts and Telecommunications
%P 126-132
%V 42
%N 4
%X A new method for extraction and tracking trajectory of dynamic wireless channel tap clusters is proposed. First, the channel impulse response (CIR) denoising is achieved by back propagation (BP) neural network in time delay-amplitude dimension. Then effective taps are clustered by <em>k</em>-means clustering algorithm. Next, density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied to remove the abnormal peak taps for every cluster. Finally, the trajectory of cluster peak taps is obtained by polynomial fitting. The simulation result shows that trajectory obtained by proposed method is approximate to geometric calculation result. Moreover, the analysis result of high speed railway measured data is consistent with the actual observations.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-298