基于RBF神经网络的室内定位算法研究  

Research on indoor location algorithm based on RBF neural network

作　　者：龚阳[1] 崔琛[1] 余剑[1] 孙从易 Gong Yang Cui Chen Yu Jian Sun Congyi(Electronic Engineering Institute, Hefei 230037, Chin)

机构地区：[1]电子工程学院,合肥230037

出　　处：《电子测量技术》2016年第10期57-60,99,共5页Electronic Measurement Technology

摘　　要：在无线传感器网络室内定位中,由于遮挡、多径效应等因素的影响,传统基于RSSI(Received Signal Strength Indicator)的定位算法存在测距不准、定位精度不高的问题。针对此问题,本文提出一种改进的基于RBF(Radial Basis Function)神经网络的室内定位算法,算法在离线阶段直接建立各参考节点接收到的RSSI值与其位置坐标的映射关系;在线阶段采集待定位节点的RSSI值,利用学习好的神经网络对待定位节点进行定位。实验结果表明,与传统RSSI定位算法相比,本文提出的定位算法具备更高的定位精度。Located in indoor environment of wireless sensor network,the traditional RSSI（Received Signal Strength Indicator）localization algorithm has the shortcomings of inaccurate distance measurement and imprecise location because of the influence of shelter and the multipath effect.Aimed to solve the problem,a RSSI localization algorithm used RBF（radial basis function）neural network is proposed.Offline stage,the mapping relation between the RSSI value that the reference node received and its spatial coordinate is established.Online stage,the RSSI value is collected and the well trained neural network is performed to locate the node without the known orientation.The experimental results show that the proposed algorithm can effectively improve the positioning accuracy compared with the traditional RSSI localization algorithm.

关 键 词：室内定位 RSSI RBF神经网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]