Relative positioning is one of the important techniques in collaborativerobotics, autonomous vehicles, and virtual/augmented reality (VR/AR)applications. Recently, ultra-wideband (UWB) has been utilized to calculatere...Relative positioning is one of the important techniques in collaborativerobotics, autonomous vehicles, and virtual/augmented reality (VR/AR)applications. Recently, ultra-wideband (UWB) has been utilized to calculaterelative position as it does not require a line of sight compared to a camerato calculate the range between two objects with centimeter-level accuracy.However, the single UWB range measurement cannot provide the relativeposition and attitude of any device in three dimensions (3D) because oflacking bearing information. In this paper, we have proposed a UWB-IMUfusion-based relative position system to provide accurate relative positionand attitude between wearable Internet of Things (IoT) devices in 3D. Weintroduce a distributed Euler angle antenna orientationwhich can be equippedwith the mobile structure to enable relative positioning. Moving average andmin-max removing preprocessing filters are introduced to reduce the standarddeviation. The standard multilateration method is modified to calculate therelative position between mobile structures. We combine UWB and IMUmeasurements in a probabilistic framework that enables users to calculatethe relative position between two nodes with less error. We have carried outdifferent experiments to illustrate the advantages of fusing IMU and UWBranges for relative positioning systems. We have achieved a mean accuracy of0.31m for 3D relative positioning in indoor line of sight conditions.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
基金supported by Samsung Advanced Institute of Technology and partly supported by the National Research Foundation of Korea (NRF)grant funded by the Korean government (MSIT) (2022R1F1A1063662).
文摘Relative positioning is one of the important techniques in collaborativerobotics, autonomous vehicles, and virtual/augmented reality (VR/AR)applications. Recently, ultra-wideband (UWB) has been utilized to calculaterelative position as it does not require a line of sight compared to a camerato calculate the range between two objects with centimeter-level accuracy.However, the single UWB range measurement cannot provide the relativeposition and attitude of any device in three dimensions (3D) because oflacking bearing information. In this paper, we have proposed a UWB-IMUfusion-based relative position system to provide accurate relative positionand attitude between wearable Internet of Things (IoT) devices in 3D. Weintroduce a distributed Euler angle antenna orientationwhich can be equippedwith the mobile structure to enable relative positioning. Moving average andmin-max removing preprocessing filters are introduced to reduce the standarddeviation. The standard multilateration method is modified to calculate therelative position between mobile structures. We combine UWB and IMUmeasurements in a probabilistic framework that enables users to calculatethe relative position between two nodes with less error. We have carried outdifferent experiments to illustrate the advantages of fusing IMU and UWBranges for relative positioning systems. We have achieved a mean accuracy of0.31m for 3D relative positioning in indoor line of sight conditions.
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.
