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Infrastructure-free indoor pedestrian tracking based on foot mounted UWB/IMU sensor fusion

机译:基于脚上安装的UWB / IMU传感器融合的无基础设施的室内行人跟踪

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Accurate indoor human localization without requiring any pre-installed infrastructure is essential for many applications, such as search and rescue in fire disaster areas or human social interaction. Ultra-wideband (UWB) is a very promising technology for accurate indoor positioning with pre-installed receivers. An infrastructure-free methodology, called Pedestrian Dead-Reckoning (PDR), which uses an inertial measurement unit (IMU), can also be used for position estimation. In this approach, the drift errors of IMU in each step length estimation are compensated based on zero-velocity update (ZUPT), zero angular rate update (ZARU) and heuristic heading drift reduction (HDR) algorithms. An accurate step detection can be achieved by relying on the data provided by accelerometers and gyroscopes. In order to further improve the accuracy, a novel approach, which combines IMU PDR and UWB ranging measurements by Extended Kalman filter (EKF) without any pre-installed infrastructure, is proposed. All the components in this approach, the IMU, the mobile station (MS) and the receiver of the UWB are mounted on the feet. The biases in the IMU measurements, which cause inaccurate step length estimation, can be compensated by range measurements provided by UWB. The performance of the normal PDR with EKF is evaluated as comparison to the proposed approach. The real test results show that the proposed approach with EKF is the most effective way to reduce the error.
机译:对于许多应用(例如火灾灾区的搜索和救援或人类社会互动),准确的室内人类定位而不需要任何预先安装的基础架构至关重要。超宽带(UWB)是一项非常有前途的技术,可通过预安装的接收器进行精确的室内定位。使用惯性测量单元(IMU)的无基础设施的方法,称为行人静坐法(PDR),也可以用于位置估计。在这种方法中,基于零速度更新(ZUPT),零角速率更新(ZARU)和启发式航向漂移减少(HDR)算法来补偿IMU在每个步长估计中的漂移误差。依靠加速度计和陀螺仪提供的数据可以实现准确的步距检测。为了进一步提高精度,提出了一种新颖的方法,该方法通过扩展卡尔曼滤波器(EKF)将IMU PDR和UWB测距结合起来,而无需任何预安装的基础结构。这种方法中的所有组件,IMU,移动台(MS)和UWB的接收器都安装在脚上。可以通过UWB提供的范围测量来补偿IMU测量中的偏差,这些偏差会导致步长估计不准确。通过与拟议方法进行比较,评估了带有EKF的常规PDR的性能。真实的测试结果表明,所提出的EKF方法是减少误差的最有效方法。

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