Multilayer ANN indoor location system with area division in WLAN environment

作     者：Mu Zhou Yubin Xu Li Tang 

作者机构：School of Electronics and Information Engineering Harbin Institute of Technology Harbin 150001 P. R. China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2010年第21卷第5期

页      面：914-926页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 12[管理学] 13[艺术学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National High Technology Research and Development Program of China (863 Program)(2008AA12Z305) 

主　　题：indoor location artificial neural network multilayer structure multi-mode relativity. 

摘      要：An indoor location system based on multilayer artificial neural network（ANN） with area division is *** characteristics of recorded signal strength（RSS）,or signal to noise ratio（SNR） from each available access points（APs）,are utilized to establish the radio map in the off-line *** in the on-line phase,the two or three dimensional coordinates of mobile terminals（MTs） are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio *** the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points,the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor ***,the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is *** also,the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division,K-nearest neighbor（KNN） and probability methods in typical office environment.