Research on Indoor Wireless Positioning Precision Optimization Based on UWB

  • Hua Guo College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Mengqi Li College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Xuejing Zhang College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Qian Liu College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
  • Xiaotian Gao College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
Keywords: UWB, positioning, neutral network, clustering, Kalman filtering

Abstract

The ultra-wide band (UWB) indoor positioning precision often has large deviations due to environmental influences. To reduce noise error and improve the UWB indoor positioning precision, this paper divides the indoor positioning into static positioning and mobile positioning, and proposes different optimization algorithms for the two positioning modes. An improved self-organizing feature mapping neural network clustering algorithm is used for static positioning. After training, the layout of the neural network is established, and each weight vector is located at the center of the input vector cluster. Experiments indicate that the fully trained neural network can effectively filter noise, and reduce the mean square error significantly within 3.0×10−3. The positioning precision is 32.39% and 17.24% higher than those of the K-mean filtering algorithm and the Kalman filtering algorithm. For mobile positioning, the optimized neural network clustering algorithm is integrated with the unscented Kalman filter (UKF) to smooth the positioning data and reduce non-line-of-sight (NLOS) error. Experiments prove that this method can effectively reduce the errors caused by the NLOS state change, and estimate the distance with a high precision for ultra-wide band positioning and tracking.

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Author Biographies

Hua Guo, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Hua Guo was born in Shandong Province, China in 1977. He received the B.E. degrees in mechanical engineering from the University of Yantai in 2000, the M.E. degree in Electronic information science and technology from Shandong University of Science and Technology in 2006, and the Ph.D. degree in Surveying instrument and system from Shandong University of Science and Technology in 2016. He is currently a senior lecturer in Department of electronic information science and technology at Shandong University of Science and Technology. His research interests are Internet of things system(IOT), embedded system and industrial robot control system.

Mengqi Li, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Mengqi Li was born in Shandong Province, China in 1995. He received his B.E. degree in electronic information science and technology from Shandong University of Science and Technology in 2017, China, where he is currently pursuing the M.E. degree in Circuits and Systems. His research interests include embedded system design, neural networks, and multiple programming languages.

Xuejing Zhang, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Xuejing Zhang was born in Shandong Province, China in 1996. He received the B.E. degree in Electronic Information Engineering from Lanzhou University of Finance and Economics in 2018, China. Now he is currently pursuing the M.E. degree in Electronic Information Engineering at Shandong University of Science and Technology. His research interests include embedded system design and Internet of Things, Linux operating system, Internet of Things.

Qian Liu, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Qian Liu was born in Shandong Province, China in 1995. She received the B.E. degree in Electronic information science and technology from Shandong First Medical University in 2017, China. She is currently pursuing the M.E. degree in Electronics and Communications Engineering at Shandong University of Science and Technology. Her research interests include embedded system design and Internet of Things, Linux operating system and Big Data Analysis.

Xiaotian Gao, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Xiaotian Gao was born in Shanxi Province, China in 1998. He received the B.E. degree in Electronics and communications from Shandong University of Science and Technology in 2019, where he is currently pursuing the M.E. Degree. His research interests include IOT and Embedded System, including Single-chip microcomputer technology and Linux System.

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Published
2020-12-24
Section
Advanced Practice in Web Engineering