基于改进SHKF算法的UWB/IMU组合定位方法

UWB/IMU combined positioning method based on improved SHKF algorithm

针对复杂环境下超宽带(UWB)无线定位系统存在非视距(NLOS)及随机误差的问题,提出一种基于改进Sage-Husa卡尔曼滤波算法(SHKF)的UWB/IMU组合定位方法。首先,设计了一种基于概率密度的提升树,将UWB/IMU特征数据的概率分布密度引入提升树的损失函数中,鉴别出NLOS信号;然后,设计了一种改进SHKF算法,根据新息变化趋势定义自适应因子,实时调整对新息误差修正的策略以调节历史噪声对当前定位的影响,进而提升UWB/IMU组合定位的稳定性和精度。实验结果表明,所提方法将NLOS信号鉴别精度提升至99.12%,定位均方根误差降低至4.30 cm,提升了复杂环境下UWB/IMU组合系统定位精度。

In view of the problem of non-line of sight(NLOS)and random error in ultra-wide band(UWB)wireless positioning system in complex environment,a UWB/IMU combined positioning algorithm based on improved Sage-Husa Kalman filter(SHKF)is proposed.First,a boosting tree based on probability density is designed,and the NLOS signals is identified by introducing the probability distribution density of UWB/IMU collected feature data into the loss function of the boosting tree.Then,an improved SHKF algorithm is designed to define an adaptive factor according to the changing trend of innovation,and adjust the strategy of correcting the error of innovation in real time to adjust the influence of historical noise on the current positioning,so as to improve the stability and accuracy of UWB/IMU combined positioning.The experimental results show that the NLOS signal identification accuracy of the proposed method is up to 99.12%,and the root mean square error of positioning is reduced to 4.30 cm,which improves the positioning accuracy of UWB/IMU integrated system in complex environment.