Precise Transceiver-Free Localization in Complex Indoor Environment  

作　　者：Rui Mao Peng Xiang Dian Zhang 

机构地区：[1]College of Computer Science and Software, Shenzhen University

出　　处：《China Communications》2016年第5期28-37,共10页中国通信（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.61202377, U1301251);National High Technology Joint Research Program of China (Grant No.2015AA015305);Science and Technology Planning Project of Guangdong Province (Grant No.2013B090500055);Guangdong Natural Science Foundation (Grant No.2014A030313553)

摘　　要：Transceiver-free object localization can localize target through using Radio Frequency(RF) technologies without carrying any device, which attracts many researchers' attentions. Most traditional technologies usually first deploy a number of reference nodes which are able to communicate with each other, then select only some wireless links, whose signals are affected the most by the transceiver-free target, to estimate the target position. However, such traditional technologies adopt an ideal model for the target, the other link information and environment interference behavior are not considered comprehensively. In order to overcome this drawback, we propose a method which is able to precisely estimate the transceiver-free target position. It not only can leverage more link information, but also take environmental interference into account. Two algorithms are proposed in our system, one is Best K-Nearest Neighbor(KNN) algorithm, the other is Support Vector Regression(SVR) algorithm. Our experiments are based on Telos B sensor nodes and performed in different complex lab areas which have many different furniture and equipment. The experiment results show that the average localization error is round 1.1m. Compared with traditional methods, the localization accuracy is increased nearly two times.Transceiver-free object localization can localize target through using Radio Frequency（RF） technologies without carrying any device, which attracts many researchers＇ attentions. Most traditional technologies usually first deploy a number of reference nodes which are able to communicate with each other, then select only some wireless links, whose signals are affected the most by the transceiver-free target, to estimate the target position. However, such traditional technologies adopt an ideal model for the target, the other link information and environment interference behavior are not considered comprehensively. In order to overcome this drawback, we propose a method which is able to precisely estimate the transceiver-free target position. It not only can leverage more link information, but also take environmental interference into account. Two algorithms are proposed in our system, one is Best K-Nearest Neighbor（KNN） algorithm, the other is Support Vector Regression（SVR） algorithm. Our experiments are based on Telos B sensor nodes and performed in different complex lab areas which have many different furniture and equipment. The experiment results show that the average localization error is round 1.1m. Compared with traditional methods, the localization accuracy is increased nearly two times.

关 键 词：indoor localization transceiver-free radio map support vector regression 

分 类 号：TN859[电子电信—信息与通信工程]