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Indoor Positioning Using Public FM and DTMB Signals Based on Compressive Sensing 预览

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摘要 Location-Based Services have become an indispensable part of our daily life, the sparsity of location finding makes it possible to estimate specific position by Compressive Sensing(CS). Using public Frequency Modulation(FM) broadcasting and Digital Television Terrestrial Multimedia Broadcasting(DTMB) signals, this paper presents an indoor positioning scheme, which is consisted of an offline stage and an online stage. In the offline stage, the Received Signal Strength(RSS) at the Reference Points(RPs) is measured, including the average and variance of public FM broadcasting and DTMB signals. In the online stage, the K-Weighted Nearest Neighbor algorithm is used to fulfill coarse positioning, which is able to narrow the selection scope of locations and choose partial RPs for accurate positioning. Then, the accurate positioning is implemented through CS. Experiment shows that the average positioning error of the proposed scheme is 1.63 m. What’s more, a CS-based method has been proposed in this paper to reduce the labor cost when collecting data. Experiment shows the average positioning error is 2.04 m, with the advantage of a 34% labor cost reduction. Experiment results indicate that the proposed system is a practical indoor positioning scheme.
出处 《中国通信:英文版》 SCIE CSCD 2019年第5期171-180,共10页 China Communications
基金 the National Natural Science Foundation of China under grant No.61571244 and No. 61871239 in part by Tianjin Research Program of Application Foundation and Advanced Technology under grant No.16YFZCSF00540 and No.18YFZCGX00480.
作者简介 Menghuan Yang,is working towards the Ph.D. degree with the College of Electronic Information and Optical Engineering, Nankai University. His current research interests include GNSS and indoor positioning technology;The corresponding author:Hong Wu,is a professor in the College of Electronic Information and Optical Engineering, Nankai University. She received her Ph.D. degree from Nankai University in 2005. Her current research interests include wireless communication technology and satellite positioning technology. She is the corresponding author of this paper,email: wuhong@ nankai.edu.cn;Zhiyang Liu,is a lecturer in the College of Electronic Information and Optical Engineering,Nankai University. He received the B.S. degree in communication engineering from Tianjin University,Tianjin,China,in 2010 and the Ph.D. degree from the City University of Hong Kong,Hong Kong,in 2014. His research interest includes wireless communication and deep learning;Shuxue Ding,is a professor with the School of Computer Science and Engineering,the University of Aizu. He is also a lecture professor with the College of Electronic Information and Optical Engineering,Nankai University. He received the Ph.D. degree in physics from the Tokyo Institute of Technology in 1996. Dr. Ding is engaged in research in a wide range of areas of mathematical and physical engineering. In particular,he has devoted himself to compressive sensing and sparse representation, machine learning,brain-style information processing, blind source separation and independent component analysis,etc;Hongzhao Peng, is a Master student with the College of Electronic Information and Optical Engineering, Nankai University. He is engaged in indoor positioning technology.
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