分布式AP选择策略在室内定位中的应用  

Application of distributed AP selection strategy in indoor location

作　　者：葛柳飞[1,2] 赵秀兰[2] 李克清[2] 戴欢[2] 张骞[1,2] 

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：《传感器与微系统》2015年第9期154-157,共4页Transducer and Microsystem Technologies

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项目(13KJB510001);苏州市物联网工程应用重点实验室项目(SZS201407)

摘　　要：由于物体遮挡和无线接入点(AP)故障等因素,导致将所有AP节点作为特征输入的定位效果并不一定最优。传统AP选择方法通过比较整体区域的信号特征参数进行选择,并没有考虑不同子区域与AP节点之间的相关性。针对该问题,提出一种分布式AP选择算法,能够有效去除较大噪声和位置分辨能力弱的AP节点。通过将室内区域划分为若干个子区域,并计算子区域与AP节点的相关性,选取相关性优的AP节点作为该子区域的训练节点,最后通过深度置信网络模型进行定位模型训练。实验结果表明:在12 m×12 m的区域范围内,该算法平均定位误差为0.415 1 m,较BP算法、RADAR算法而言,平均定位误差分别降低了35.95%,46.78%,运行时间分别减少了22.8%,32.9%。The factors of shades and fault nodes will cause that location effect is not the optimal,when all AP nodes are as feature inputs. The traditional methods select AP nodes by comparing signal feature of the whole area,without considering correlation between different sub area and AP nodes. Aiming at this problem,put forward a distributed AP selection algorithm,which can effectively remove at AP nodes with big noise and weak ability of position resolution. This method divides indoor area into several sub areas and calculates the correlation between the sub area and AP nodes for selecting AP nodes with optimal correlation as training nodes of this sub area. Deep belief networks（ DBN） model is used for training location model. According to the result of the experiments,average location error of the proposed model is 0. 415 1 m at the range of 12 m × 12 m. At the same time,comparing with BP algorithm,RADAR algorithm,the proposed model performs better in wireless indoor location,which reduces positioning error by 35. 95 % and 46. 78 % and decreases running time by 22. 8 % and 32. 9 %respectively.

关 键 词：无线接入点 室内定位 相关性 深度置信网络模型 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]