Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder

• Published in: International journal of distributed sensor networks Vol. 15; no. 7; p. 155014771986099
• Main Authors: Fu, Yulu, Liu, Ran, Zhang, Hua, Liang, Gaoli, Rehman, Shafiq ur, Liu, Lixiang
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.07.2019; Wiley
• Subjects: continuous tracking; Ultra-high frequency radio-frequency identification; particle filter; velocity matching; density-based spatial clustering of applications with noise clustering
• ISSN: 1550-1329; 1550-1477; 1550-1477
• DOI: 10.1177/1550147719860990

Due to the unique and contactless way of identification, radio-frequency identification is becoming an emerging technology for objects tracking. As radio-frequency identification does not provide any distance or bearing information, positioning using radio-frequency identification sensor itself is challenging. Two-dimensional laser range finders can provide the distance to the objects but require complicated recognition algorithms to acquire the identity of object. This article proposes an innovative method to track the locations of dynamic objects by combining radio-frequency identification and laser ranging information. We first segment the laser ranging data into clusters using density-based spatial clustering of applications with noise (DBSCAN). Velocity matching–based approach is used to track the location of object when the object is in the radio-frequency identification reading range. Since the radio-frequency identification reading range is smaller than a two-dimensional laser range finder, velocity matching–based approach fails to track location of the object when the radio-frequency identification reading is not available. In this case, our approach uses the clustering results from density-based spatial clustering of applications with noise to continuously track the moving object. Finally, we verified our approach on a Scitos robot in an indoor environment, and our results show that the proposed approach reaches a positioning accuracy of 0.43 m, which is an improvement of 67.6% and 84.1% as compared to laser-based and velocity matching–based approaches, respectively.