基于自适应GRNN的无线室内定位算法  

Wireless Indoor Location Algorithm Based on Adaptive GRNN

作　　者：葛柳飞[1,2] 李克清[2] 戴欢[2] 

机构地区：[1]中国矿业大学计算机科学与技术学院,江苏徐州221116 [2]常熟理工学院计算机科学与工程学院,江苏常熟215500

出　　处：《计算机工程》2016年第6期81-85,90,共6页Computer Engineering

基　　金：国家自然科学基金资助项目(61300186);江苏省高校自然科学研究面上基金资助项目(13KJB510001);苏州市物联网工程应用重点实验室基金资助项目(SZS201407)

摘　　要：室内信号强度波动的随机性使广义回归神经网络(GRNN)难以选择最优参数建立定位模型并预测目标位置。为此,提出一种自适应广义回归神经网络的定位算法。利用改进的人工蜂群算法对广义回归神经网络进行参数优化,并将其应用于无线室内定位,建立无线信号特征与目标位置信息的映射关系,利用建立的映射关系预测目标位置,降低信号强度波动的随机性对定位精度的影响。实验结果表明,在12 m×12 m的区域范围内,该算法的平均定位误差为0.65 m,与基于蜂群算法的GRNN以及基于粒子群算法的GRNN相比,该算法的定位准确率分别提高了21.3%和23.1%,且收敛速度较快。与路径损耗模型和BP神经网络相比,该算法的定位准确率分别提高了17.86%和3.1%,能够有效提高定位精度。The randomness of signal strength with stochastic fluctuation makes Generalized Regression Neural Network（GRNN） difficult to choose the optimal parameters to establish the location model and predict the target location.For this reason,a location algorithm with adaptive GRNN is put forward.The method introduces the Improved Artificial Bee Colony Algorithm（IABC） to optimize the parameter of GRNN,and is applied to wireless indoor location for the mapping relationship between the signal characteristics and the target location,which can predict the target location and reduces the influence of the randomness of signal strength with stochastic fluctuation on location accuracy.Experimental results show that average location error of the proposed algorithm is 0.65 m at the range of 12 m×12 m.Compared with GRNN based on Artificial Bee Colony（ABC-GRNN）,GRNN based on Particle Swarm Optimization（PSO-GRNN）,the location accuracy of this algorithm is increased by 21.3% and 23.1%,and has the fastest convergence speed.At the same time,compared with the path loss model,BP neural network,location accuracy of the algorithm respectively is increased by 17.86% and 3.1%,which can effectively improve the location accuracy.

关 键 词：信号强度 室内定位 广义回归神经网络 人工蜂群 定位准确率 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]