基于DL-LSTM的UWB/INS室内定位算法

UWB/INS indoor positioning algorithm based on DL-LSTM

为了提高超宽带(UWB)室内定位系统的定位精度,提出了一种基于双层长短期记忆神经网络(DL-LSTM)的UWB室内定位算法。利用UWB系统和惯性导航系统(INS)采集非视距(NLOS)环境下的定位数据,根据该数据在NLOS环境下传播时的深度特征建立DL-LSTM模型,然后将数据输入到网络中进行训练。第一层网络用于减小NLOS误差对系统定位精度的影响,第二层网络对UWB/INS组合系统进行位置预测,进一步提高定位的精度。实验结果表明:本文算法可以有效减小NLOS误差的影响,可达到厘米(cm)级定位要求。

In order to improve the positioning precision of ultra-wideband(UWB)indoor positioning system,an ultra-wideband indoor positioning algorithm based on a double-layer long-short term memory(DL-LSTM)neural network is proposed.The indoor positioning data is collected by the UWB and inertial navigation system(INS).According to its depth characteristics in the non-line-of-sight environment,the DL-LSTM model is established.The first network layer of DL-LSTM model is used to reduce the influence of non-line of sight(NLOS)error on system positioning precision.Then UWB and INS signal is combined in the second network.The second network layer is used to predict the position of UWB/INS,further to improve the positioning precision.The experimental results show that the influence of NLOS error can be reduced effectively by the proposed algorithm,and the location-solving trajectory is closer to the real trajectory,which can meet the requirements of centimeter-level positioning.