基于UWB和IMU紧组合的室内定位导航算法

针对超宽带(UWB)在室内复杂环境中定位导航精度低,受非视距(NLOS)误差影响严重,且无法提供目标姿态信息的问题,提出一种基于UWB和惯性测量单元(IMU)紧组合的室内定位导航算法。以位置、速度、四元数、加速度计偏差和陀螺仪偏差为状态向量,通过扩展卡尔曼滤波算法融合UWB和IMU测量信息,加速度计偏差校正速度和位置,陀螺仪偏差校正四元数;用测量残差计算量测噪声因子,组成残差矩阵,动态调整量测噪声协方差矩阵,抑制NLOS误差对定位导航的影响。结果表明,在室内复杂环境下,基于UWB和IMU紧组合的定位导航算法比仅使用UWB定位时LS-Taylor算法精度提高了88.6%,增强了系统抗NLOS误差的能力,提高了动态定位精度,并能得到较准确的姿态信息,具有良好的实用性和鲁棒性。

Indoor positioning and navigation algorithm based on UWB and IMU tightly coupled

A new algorithm is proposed in this work, to solve the disadvantages of UWB(Ultra-wideband) in the complex indoor environment, such as low positioning and navigation accuracy, serious effect of NLOS(Non-line-of-sight) error, and inability to provide target attitude information, which takes position, velocity, quaternion, bias errors of accelerometer and gyroscope as state vectors, fuses UWB and IMU(Inertial measurement unit) measurement information through EKF(Extended Kalman filter) algorithm, corrects velocity, position, quaternion with the bias errors of accelerometer and gyroscope, the quaternion after filtering to calculate the rotation matrix and attitude information. Then, the residual error is used to calculate the measurement noise factor, the residual matrix is composed, and the covariance matrix of the observation noise is dynamically adjusted to suppress the influence of NLOS error on positioning and navigation. The results show that the positioning and navigation algorithm based on the combination of UWB and IMU improves the accuracy of 88.6% compared with the LS-Taylor algorithm in the complex indoor environment, which enhances the system''s ability to resist NLOS error, improves the dynamic positioning accuracy, and can get more accurate attitude information, which has better practicability and robustness.