UWB Location Algorithm Based on BP Neural Network

In order to solve the problem that in the traditional trilateral positioning algorithm, the final positioning error is large when there is a certain error in the measured three-sided distance, a UWB positioning algorithm based on Back Propagation (BP) neural network is proposed. The algorithm utilizes the fast learning characteristic and the ability of approximating any non-linear mapping of neural network, and realizes the location of the mobile label through the TOA measurement value provided by the base station and the BP neural network. By comparing the traditional trilateral positioning algorithm, the BP neural network algorithm based on four distance inputs and the BP neural network algorithm based on four distance inputs with trilateral positioning coordinates, it can be seen that the positioning error of traditional trilateral positioning algorithm is 30 cm, and the positioning error of the positioning algorithm based on the BP neural network proposed in this paper is 10 cm. The positioning algorithm proposed in this paper can effectively reduce the impact of distance measurement error and non-line-of-sight propagation during wireless signal transmission, and obviously improve the positioning accuracy of UWB positioning. The UWB positioning algorithm based on BP neural network proposed in this paper has been used to locate the vehicle in the process of automatic parking and has better real-time and accuracy.

• Record URL:
  • https://doi.org/10.4271/2018-01-1605
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Zhuo, Guirong
  • Xue, Ruonan
• Conference:
  • Intelligent and Connected Vehicles Symposium
  • Location: Kunshan City Jiangsu, China
  • Date: 2018-8-14 to 2018-8-15
• Publication Date: 2018-8-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Backpropagation; Location; Mathematical models; Neural networks; Parking; Positioning
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01712772
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2018-01-1605
• Files: TRIS, SAE
• Created Date: Jul 29 2019 11:02AM