摘要
针对传统WiFi-行人航迹推算(PDR)定位算法中WiFi信号提供的初始位置存在误差及PDR也存在累积误差等因素导致定位精度下降的问题,提出了一种基于卷积神经网络(CNN)的活动识别辅助PDR室内定位算法。在实验区域布置若干接入点(AP),在特征区域得出的AP信号强度阈值RSSI_θ作为触发器,当移动设备检测到信号强度(RSSI)大于RSSI_θ时,则触发该算法,判断用户是否在特征区域内活动,是否与当前特征点活动匹配;若匹配,则将此时位置校正为当前特征点所属位置,否则继续PDR定位。实验结果表明:相较于传统WiFi-PDR算法,该算法在特征点处定位精度提高了33. 5%,且整体定位精度提高了24. 6%。
Aiming at the problem that in traditional WiFi-pedestrian track reckoning( PDR) localization algorithm,there are some errors in the initial position provided by WiFi,and there are accumulative errors in PDR positioning itself. These two factors lead to the decline of positioning accuracy. An activity recognition aided PDR indoor localization algorithm based on convolutional neural network( CNN) is proposed. Several access points( AP)are arranged in the experimental area. The AP signal intensity threshold RSSI_θ obtained in the feature area is used as a trigger. When a mobile device detects a signal( RSSI) greater than RSSI_θ,then it triggers this algorithm to determine whether the user is active in the feature area and whether it matches the current feature point activity,if so,The position is corrected to the position of the current feature point,otherwise,continue PDR. The experimental results show that compared with the traditional WiFi-PDR localization algorithm,the precision of the feature points is increased by 33. 5 %. And the overall positioning precision is improved by 24. 6 %.
作者
冯描芬
余敏
薛峰
FENG Miaofen;YU Min;XUE Feng(College of Computer Information and Engineering,Jiangxi Normal University,Nanchang 330022,China)
出处
《传感器与微系统》
CSCD
2020年第4期118-120,128,共4页
Transducer and Microsystem Technologies
基金
国家重点研发计划资助项目(2016YFB0502204)。
