A combined algorithm for tunnel personnel localization based on error areal division. (February 2022)

• Record Type: Journal Article
• Authors: Xu, Jian; Wang, Haiying; Ren, Yiqing; Zhang, Yingzhi
• Is Part Of: International journal of distributed sensor networks. Volume 18:Number 2(2022)
• Publication Date: 2022-02
• Subjects: Tunnel personnel positioning -- average positioning error -- combined localization algorithm -- error areal division; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501477211065936
• Languages: English
• ISSNs: 1550-1329

Abstract

In a tunnel personnel positioning system, the average positioning error is typically used to reflect the precision and accuracy of the positioning algorithm. However, in practical applications, the error areal distribution is generally non-uniform. The positioning errors of some areas are greater than the average positioning errors, which lead to a low positioning accuracy. In this study, based on the simulation of the positioning error areal distributions of the quadrilateral centroid localization and maximum likelihood estimation algorithms, the causes for large positioning errors in upper and lower edges and four corners were analyzed. In addition, a combined localization algorithm based on error areal division was proposed to improve the average positioning accuracy. Thus, the quadrilateral centroid localization algorithm was used for the middle section of the tunnel personnel location area, the maximum likelihood estimation algorithm was used for the upper and lower edges, and the centroid algorithm was used for the four corners. The simulation results show that the average positioning errors of the combined localization algorithm were reduced by 5.56% and 44.76% when compared with those of the quadrilateral centroid localization and maximum likelihood estimation algorithms, respectively, while the maximum positioning error was reduced by 46.45% and 42.81%, respectively.