华东交通大学学报2017，Vol.34Issue(4)：137-142,6.
基于GRNN神经网络的ZigBee室内定位算法研究
Study on Indoor Location Algorithm of ZigBee Based on GRNN Neural Network
摘要
Abstract
In response to the problem that the localization algorithm based on the wireless signal propagation loss model with fixed parameters can't remove ranging errors induced by multipath propagation effects and environmental complexity.This study adopted GRNN neural network to fit the RSSI value and distance value,and then get the mapping model of RSSI value and distance value.It adopted the RSSI value as the input layer of the trained GRNN neural network and derived the RSSI value in the output layer.Finally,the weighted centroid algorithm was applied to locate the node.It finds that the algorithm is simple and well-behaved,and does not require additional hardware.Through the simulation and experiment results on the MATLAB and ZigBee,compared with the localization algorithm based on the path loss model and BP neural network,the proposed algorithm can provide better localization results.关键词
室内定位/广义回归神经网络/信号接收强度/加权质心/无线传感器网络Key words
indoor location/GRNN neural networks/received signal strength indication/weighted centroid/WSN分类
信息技术与安全科学引用本文复制引用
邓胡滨,许峰,周洁..基于GRNN神经网络的ZigBee室内定位算法研究[J].华东交通大学学报,2017,34(4):137-142,6.基金项目
江西省自然科学基金项目(20142BAB217019) （20142BAB217019）
华东交通大学校立科研课题(12XX06) （12XX06）
