融合UWB和IMU数据的新型室内定位算法  

Novel Indoor Positioning Algorithm by Fusing Data of UWB and IMU

作　　者：周军[1] 魏国亮 田昕 王甘楠 ZHOU Jun;WEI Guo-liang;TIAN Xin;WANG Gan-nan(Department of Control Science and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;College of Science,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093 [2]上海理工大学理学院,上海200093

出　　处：《小型微型计算机系统》2021年第8期1741-1746,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61873169)资助。

摘　　要：针对室内定位中的非视距(Non-Line-of-Sight,NLOS)现象,提出一个新型算法进行识别,同时有效缓解其影响.主要通过超宽带(Ultra-Wideband,UWB)定位系统与惯性导航系统(Inertial Navigation System,INS)的信息修正非视距误差,获得较高的定位精度.首先,在离线阶段获得不同障碍物下的NLOS误差概率分布曲线;其次,利用惯性测量单元(Inertial Measurement Unit,IMU)的预测位置及NLOS误差概率曲线修正测量距离;最后,利用卡尔曼滤波(Kalman Filtering,KF)融合步行者航迹推算(Pedestrian Dead Reckoning,PDR)的INS位置和经过改进最小二乘法(Least Square,LS)处理后UWB定位系统的位置,并更新NLOS误差获得更准确的位置估计.通过仿真和实验证实了提出的定位算法可以有效缓解NLOS误差,提升定位性能,实现在NLOS影响下的高精度定位.Aiming at the phenomenon of Non-Line-of-Sight(NLOS)in indoor positioning,a novel algorithm is proposed to deal with it.The NLOS errors are mainly corrected by the information of the ultra-wideband(UWB)positioning system and the inertial navigation system(INS),and the higher positioning accuracy is obtained.First,the NLOS error probability distribution curves under different obstacles are obtained in the offline phase;second,the measured distances are corrected by the predicted position of the inertial measurement unit(IMU)and the NLOS error probability curves;finally,kalman filtering(KF)is used to combine the position of INS under Pedestrian Dead Reckoning(PDR)and the position of the UWB positioning system calculated by improved least squares(LS),and updates the NLOS error to obtain a more accurate position estimate.Simulations and experiments confirmed that the proposed positioning algorithm can effectively alleviate the NLOS errors,improve positioning performance and achieve high-precision positioning under the influence of NLOS.

关 键 词：室内定位 卡尔曼滤波 概率分布曲线 非视距 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]