He Chengyang, Tang Chao, Yu Chengpu
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, China.
Sensors (Basel). 2020 Jun 21;20(12):3514. doi: 10.3390/s20123514.
The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.
惯性测量单元与超宽带信号（IMU-UWB）组合的室内定位系统具有非线性状态方程和线性测量方程。为了提高计算效率以及在估计精度方面的定位性能，通过结合传统卡尔曼滤波和容积卡尔曼滤波，提出了联邦导数容积卡尔曼滤波（FDCKF）方法。通过实施所提出的FDCKF方法，可以有效地融合超宽带（UWB）和惯性测量单元（IMU）的观测数据；特别是，惯性测量单元可以通过超宽带进行连续校准，从而不会产生累积误差。最后，通过数值模拟证明了所提算法的有效性，其中分别将FDCKF与联邦容积卡尔曼滤波器（FCKF）和联邦无迹卡尔曼滤波器（FUKF）进行了比较。
