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RBUKF Sensor Data Fusion for Localization of Unmanned Mobile Platform

机译:RBUKF传感器数据融合用于无人移动平台的本地化

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Due to the limited localization precision of single sensor, a sensor data fusion is introduced based on Rao-Blackwellization Unscented Kalman Filter (RBUKF) that fuses the sensor data of a GPS receiver, one gyro and one compass. RBUKF algorithm is compared with that of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) in this study. The experimental results show that the RBUKF algorithm can more effectively improve tracking accuracy and reduce computational complexity than the other algorithms and has practical significance.
机译:由于单个传感器的定位精度有限,因此基于Rao-Blackwellization无味卡尔曼滤波器(RBUKF)引入了传感器数据融合,该融合融合了GPS接收器,一个陀螺仪和一个罗盘的传感器数据。本研究将RBUKF算法与扩展卡尔曼滤波器(EKF)和无味卡尔曼滤波器(UKF)进行了比较。实验结果表明,与其他算法相比,RBUKF算法可以更有效地提高跟踪精度,降低计算复杂度,具有实际意义。

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