基于BP神经网络和泰勒级数的室内定位算法研究  

Research on Indoor Location Technology Based on Back Propagation Neural Network and Taylor Series

作　　者：张会清[1] 石晓伟[1,2] 邓贵华[1] 高学金[1] 任明荣[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124 [2]中国广东核电集团中科华核电技术研究院北京分院,北京100086

出　　处：《电子学报》2012年第9期1876-1879,共4页Acta Electronica Sinica

基　　金：国家科技重大专项(No.2009ZX05039-003)

摘　　要：在研究分析室内无线信号传播特性和传统的室内定位算法的基础上,提出了用BP神经网络来拟合室内无线信号传播模型,避免了对无线信号传播模型中参数A和n的不精确估计.在训练完成的BP神经网络的输入层输入接收信号强度值RSSI(Received Signal Strength Indicator),在输出层即可得到对应的距离值,再利用泰勒级数展开法确定盲节点的坐标位置.最终通过Matlab仿真和ZigBee平台实验验证了算法的可行性和有效性.Based on lots of research and analysis on indoor radio signal propagation features and the traditional indoor location algorithms,a new method that uses BP（Back Propagation） neural network to fit the indoor radio signal propagation model is proposed,which avoids inaccurately estimating the parameters A and n in the indoor radio signal propagation model.Distance value proportional to the RSSI（Received Signal Strength Indicator） input through the well-trained BP neural network is obtained,and then Taylor series expansion algorithm is used to determine the coordinates of the blind node.Finally,the simulation and experiment results on the ZigBee platform verify the feasibility and effectiveness of the proposed algorithm.

关 键 词：室内定位 BP神经网络 RSSI(Received SIGNAL Strength Indicator) ZigBee 泰勒级数 

分 类 号：TN393[电子电信—物理电子学]