基于CNN的超宽带/惯性导航室内定位算法

UWB inertial navigation indoor positioningalgorithm based on CNN

为了减小非视距(NLOS)误差对超宽带(UWB)室内定位系统定位精度的影响,提出了一种基于卷积神经网络(CNN)的UWB室内定位算法。利用UWB系统采集NLOS环境下的室内定位数据,根据信号在NLOS环境下传播时的误差特性建立CNN模型,将数据输入网络进行训练,以减小NLOS误差对系统定位的影响;结合惯性导航系统(INS),用扩展卡尔曼滤波(EKF)对UWB/INS组合系统进行位置估计,进一步提高定位的精度。实验结果表明:该算法可以有效减小NLOS误差的影响,定位解算轨迹更加贴近真实轨迹,可达到厘米级定位要求。

In order to eliminate effects of non-line-of-sight(NLOS)error on positioning precision of ultra-wideband(UWB)indoor positioning system,an UWB indoor positioning algorithm based on convolutional neural network(CNN)is proposed.By using UWB system to collect the indoor positioning data and according to the error characteristics of signal propagation in the NLOS environment,a CNN model is established and the data is input into the network for training,so as to reduce the influence of NLOS error.Combined with the inertial navigation system(INS),the extended Kalman filter(EKF)is used to fuse the position estimation of UWB/INS system to further improve the accuracy of positioning.The experimental results prove that the proposed algorithm can effectively reduce the influence of NLOS error,and the location-solving trajectory is closer to the real trajectory,which can meet the requirements of centimeter(cm)-level positioning.