摘要
由于室内环境复杂多变,存在着严重的非视距(NLOS)和多径效应,利用传统的指纹定位技术会造成较大的定位误差。针对此问题,利用超宽带(UWB)信号测距信息准确、波动小的特点,将测距值作为指纹量,提出一种基于稀疏自编码器(SAE)与随机森林(RF)相结合的三维室内定位方法。利用SAE提取出更具鲁棒性的特征值,将此特征值作为深度神经网络(DNN)回归网络的输入,得到目标点的估计定位坐标。针对环境变化导致的旧数据库无法匹配新采集指纹量的问题,利用测距值作为RF回归模型的输入对估计定位坐标进行定位误差修正。实验结果表明:提出的SAE-RF三维定位方法与其他指纹定位方法相比,更适合动态复杂的室内环境,定位精度更高。
Due to complexity and variability of indoor environment and serious non-line of sight(NLOS)and multipath effect,traditional fingerprint positioning technology will cause large positioning errors.Aiming at this problem,using the characteristics of ultra wide band(UWB)signal ranging information accuracy and small fluctuation,using the ranging value as the fingerprint,a three-dimensional indoor localization method based on sparse auto encoder(SAE)and random forest(RF)is proposed.Firstly,more robust eigenvalue are extracted by SAE,and then the eigenvalue are used as the input of the deep neural network(DNN)regression network to obtain the estimated positioning coordinates of the target nodes.At the same time,the old database caused by environmental changes can not match the new acquired fingerprints,and the ranging value is used as the input of the random forest regression model to correct the positioning error of predict positioning coordinates.The experimental results show that the proposed SAE-RF positioning method is more suitable for dynamic and complex indoor environments than the other fingerprint positioning methods and the positioning precision is higher.
作者
李世银
朱媛
刘江
王晓明
阳媛
LI Shiyin;ZHU Yuan;LIU Jiang;WANG Xiaoming;YANG Yuan(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China)
出处
《传感器与微系统》
CSCD
北大核心
2021年第8期46-49,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61771474)
徐州重点研发计划资助项目((社会发展)KC18171)
国家青年科学基金资助项目(61601123)。
关键词
指纹定位
稀疏自编码器
随机森林
超宽带
fingerprinting localization
sparse autoencoder(SAE)
random forest(RF)
ultra wide band(UWB)
