A High-Precision Map Matching Algorithm Based on Nonlinear Filtering Optimization for IOT Industrial Park

• Published in: IEEE sensors journal Vol. 24; no. 2; p. 1
• Main Authors: Li, Wengang, Chen, Tianfang, Han, Jiadong, Wang, Liujiang, Huang, Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Bayesian estimation; high precision positioning; Industrial parks; map matching; Matching; particle filtering; Prediction algorithms; Predictive models; Roads; Sensors; Trajectory; Ultrawideband; Wireless communication
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3339157

In order to solve the problem that the IoT wireless positioning technology is easily affected by the environment, which leads to the unsatisfactory positioning effect, this paper proposes the Improving Particle Filter-Map Matching Algorithm (IPF-MM). Firstly, we propose to use the particle filter algorithm to filter the positioning trajectory from the wireless positioning base station. In this method, we combine the particle filter algorithm and outdoor map information and we use the centerline of the road to update the weight of the particles to improve positioning accuracy. Secondly, when the amount of data of positioning track points is too large, we set the threshold for the number of anchor tracks and reduce the number of positioning points by multiples to improve positioning efficiency. In the simulation experiment, the average relative error of the UWB positioning track is reduced by 47.7% after using the IPF-MM. Besides, the IPF-MM reduces the running time by 42.9% compared with PF-MM, which indicates that the IPF-MM can improve both the accuracy and efficiency of positioning.