在超宽带测距定位中,实现非视距(non-line of sight,NLOS)传播的正确鉴别与消除具有非常重要的意义。提出以实测数据为基础,采用多参数的机器学习方法对超宽带信号进行NLOS鉴别和测距误差消除研究,并对波形特征参数的选取进行了分析和...在超宽带测距定位中,实现非视距(non-line of sight,NLOS)传播的正确鉴别与消除具有非常重要的意义。提出以实测数据为基础,采用多参数的机器学习方法对超宽带信号进行NLOS鉴别和测距误差消除研究,并对波形特征参数的选取进行了分析和优化。数据结果表明,相比较传统NLOS鉴别方法,该方法不需要先验知识、实用性强,在鉴别的准确度和误差消除方面有较大的提升。展开更多
High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based...High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.展开更多
60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data...60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.展开更多
超宽带(Ultra Wide Band,UWB)室内定位系统的定位性能主要受信号非视距(None Line Of Sight,NLOS)传播影响。为此该文提出一种基于信道统计量(Channel Statistics Information,CSI)的信道NLOS状态检测法。该方法首先在IEEE802.15.4a信...超宽带(Ultra Wide Band,UWB)室内定位系统的定位性能主要受信号非视距(None Line Of Sight,NLOS)传播影响。为此该文提出一种基于信道统计量(Channel Statistics Information,CSI)的信道NLOS状态检测法。该方法首先在IEEE802.15.4a信道模型下对均方根时延扩展和平均超量延迟的概率分布函数进行建模,作为信道标准分布。再以信道瞬时分布与标准分布间的KL散度为检验统计量做似然比检验(Likelihood Ratio Test,LRT)来鉴别信道状态。同时提出一种基于LRT的定位算法LRT-Chan算法。该算法能有效利用受NLOS污染的测距数据提高定位精度。仿真结果表明:LRT信道状态检测法在全部UWB信道中都能获得较高检测准确率;在定位锚点(Anchor Node,AN)分布不理想的NLOS环境中LRT-Chan算法也能取得较高定位精度。展开更多
针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信...针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信息辅助UWBNLOS识别,并通过UWB视距(LineofSight,LOS)测距值消除LiDAR同时定位与建图(Simultaneous Localization and Mapping,SLAM)过程中的累计误差,从而提高室内融合定位精度和鲁棒性。首先,采用八叉树对LiDAR点云进行处理,根据UWB基准站位置信息构建测距方向,并从LiDAR点云中提取测距方向上相关区域的点云数据。然后,通过3D Alpha Shape算法对所提取点云中可能阻碍UWB信号传播的障碍物进行轮廓提取。此外,根据分析提取的障碍物轮廓和UWB测距方向的空间关系,以此有效判定UWB信号是否存在NLOS测距情况。最后,剔除UWB测距过程中存在的NLOS测距值,通过紧组合方式,采用扩展卡尔曼滤波(EKF)将UWB LOS测距值和LiDAR SLAM的定位信息进行融合解算,消除LiDAR SLAM定位结果中的累积误差,以此提高融合定位精度和鲁棒性。为验证本文所提出的融合定位算法的有效性,通过搭建的融合定位实验平台在教学楼大厅进行了NLOS静态识别实验,在地下停车场进行了动态NLOS识别与动态定位实验。实验结果表明,该方法能够显著提高在室内复杂环境中的NLOS识别与定位的准确性,相较于单传感器定位与UWB原始测距值与LiDAR SLAM紧组合EKF的定位方法,NLOS识别准确率为93.22%,定位精度分别提高了49.24%、47.03%、96.13%,定位误差为0.067 m,实现了亚分米级室内定位。展开更多
文摘在超宽带测距定位中,实现非视距(non-line of sight,NLOS)传播的正确鉴别与消除具有非常重要的意义。提出以实测数据为基础,采用多参数的机器学习方法对超宽带信号进行NLOS鉴别和测距误差消除研究,并对波形特征参数的选取进行了分析和优化。数据结果表明,相比较传统NLOS鉴别方法,该方法不需要先验知识、实用性强,在鉴别的准确度和误差消除方面有较大的提升。
基金supported in part by the National Natural Science Foundation of China under Grant No.61771474in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243+2 种基金in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT)。
文摘High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.
基金supported by National Natural Science Foundation of China(No.62101298)Collaborative Education Project between Industry and Academia,China(22050609312501)。
文摘60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.
文摘超宽带(Ultra Wide Band,UWB)室内定位系统的定位性能主要受信号非视距(None Line Of Sight,NLOS)传播影响。为此该文提出一种基于信道统计量(Channel Statistics Information,CSI)的信道NLOS状态检测法。该方法首先在IEEE802.15.4a信道模型下对均方根时延扩展和平均超量延迟的概率分布函数进行建模,作为信道标准分布。再以信道瞬时分布与标准分布间的KL散度为检验统计量做似然比检验(Likelihood Ratio Test,LRT)来鉴别信道状态。同时提出一种基于LRT的定位算法LRT-Chan算法。该算法能有效利用受NLOS污染的测距数据提高定位精度。仿真结果表明:LRT信道状态检测法在全部UWB信道中都能获得较高检测准确率;在定位锚点(Anchor Node,AN)分布不理想的NLOS环境中LRT-Chan算法也能取得较高定位精度。
文摘针对室内环境中超宽带(Ultra-Wideband,UWB)信号易受障碍物遮挡导致非视距(Non Line of Sight,NLOS)误差的问题,本文提出了一种基于激光雷达(Light Detection And Ranging,LiDAR)点云识别UWB NLOS的融合定位方法,该方法利用LiDAR点云信息辅助UWBNLOS识别,并通过UWB视距(LineofSight,LOS)测距值消除LiDAR同时定位与建图(Simultaneous Localization and Mapping,SLAM)过程中的累计误差,从而提高室内融合定位精度和鲁棒性。首先,采用八叉树对LiDAR点云进行处理,根据UWB基准站位置信息构建测距方向,并从LiDAR点云中提取测距方向上相关区域的点云数据。然后,通过3D Alpha Shape算法对所提取点云中可能阻碍UWB信号传播的障碍物进行轮廓提取。此外,根据分析提取的障碍物轮廓和UWB测距方向的空间关系,以此有效判定UWB信号是否存在NLOS测距情况。最后,剔除UWB测距过程中存在的NLOS测距值,通过紧组合方式,采用扩展卡尔曼滤波(EKF)将UWB LOS测距值和LiDAR SLAM的定位信息进行融合解算,消除LiDAR SLAM定位结果中的累积误差,以此提高融合定位精度和鲁棒性。为验证本文所提出的融合定位算法的有效性,通过搭建的融合定位实验平台在教学楼大厅进行了NLOS静态识别实验,在地下停车场进行了动态NLOS识别与动态定位实验。实验结果表明,该方法能够显著提高在室内复杂环境中的NLOS识别与定位的准确性,相较于单传感器定位与UWB原始测距值与LiDAR SLAM紧组合EKF的定位方法,NLOS识别准确率为93.22%,定位精度分别提高了49.24%、47.03%、96.13%,定位误差为0.067 m,实现了亚分米级室内定位。
