Application of LSTM Network to Improve Indoor Positioning Accuracy

Sensors

Xiangye Zeng¹  Dongqi Gao¹  Jingyi Wang²  Yanmang Su² 

[1] Hebei Key Laboratory of Advanced Laser Technology and Equipment, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China;Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China;

关键词: indoor positioning;    ultra-wide band (UWB);    LSTM;    ranging error prediction;    least squares;    deep learning;   

DOI  :  10.3390/s20205824

来源: DOAJ

【 摘 要 】

Various indoor positioning methods have been developed to solve the “last mile on Earth”. Ultra-wideband positioning technology stands out among all indoor positioning methods due to its unique communication mechanism and has a broad application prospect. Under non-line-of-sight (NLOS) conditions, the accuracy of this positioning method is greatly affected. Unlike traditional inspection and rejection of NLOS signals, all base stations are involved in positioning to improve positioning accuracy. In this paper, a Long Short-Term Memory (LSTM) network is used while maximizing the use of positioning equipment. The LSTM network is applied to process the raw Channel Impulse Response (CIR) to calculate the ranging error, and combined with the improved positioning algorithm to improve the positioning accuracy. It has been verified that the accuracy of the predicted ranging error is up to centimeter level. Using this prediction for the positioning algorithm, the average positioning accuracy improved by about 62%.

【 授权许可】

Unknown