%0 Journal Article
%T 基于集成深度神经网络的室内无线定位<br>Indoor wireless positioning based on ensemble deep neural network
%A 沈冬冬
%A 周风余
%A 栗梦媛
%A 王淑倩
%A 郭仁和&lt
%A br&gt
%A Dongdong SHEN
%A Fengyu ZHOU
%A Mengyuan LI
%A Shuqian WANG
%A Renhe GUO
%J 山东大学学报（工学版）
%D 2018
%R 10.6040/j.issn.1672-3961.0.2018.169
%X 针对传统无线定位模型对指纹数据库容错性低、抗噪能力弱等问题,提出一种基于数据融合的集成深度神经网络无线定位方法,从原始指纹数据库中按照一定比例随机取样生成各基学习器的训练数据,能够有效克服异常样本与有噪数据对无线定位系统带来的干扰;在指纹数据库构建过程中,提出Gauss-Occupied (G-O)数据扩充方法以解决无线指纹数据库样本容量小的局限,大幅度降低人工采集的成本,进一步提高样本空间的表征范围。试验结果表明:提出的模型不仅能够有效提高无线定位系统的平均定位精度与抗噪能力,而且能够明显降低定位过程中出现的单点最大误差。<br>Because of the low fault tolerance and weak anti-noise ability of fingerprint database in traditional wireless positioning model, an ensemble deep neural network wireless positioning method based on data fusion was proposed. This method could effectively overcome the interference caused by abnormal samples and noisy data on the wireless positioning system by sampling from the original fingerprint database randomly to generate train data for each base learner. During the process of fingerprint database construction, the Gauss-Occupied (G-O) data expansion method was proposed to solve the limitation of the small sample size of the wireless fingerprint database and decrease the cost of manual acquisition sharply, which increased the scope of the sample′s characterization. The results of the experiment showed that the proposed ensemble deep neural network wireless positioning model could not only improve the average positioning accuracy and the anti-noise ability of the wireless positioning system, but also reduce the maximum single point error in the positioning process.
%K 无线指纹定位
%K 数据集扩充
%K 人工采集
%K 深度神经网络
%K 集成学习
%K &lt
%K br&gt
%K wireless fingerprint positioning
%K data set expansion
%K artificial collection
%K deep neural network
%K ensemble learning
%U http://gxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1672-3961.0.2018.169