RF-OSFBLS: An RFID reader-fault-adaptive localization system based on online sequential fuzzy broad learning system

摘要

Indoor localization technology has recently attracted an increasing attention in research, among which radio frequency identification (RFID) technology has become a preferred solution due to its advantages in non-line of sight, non-contact and rapid identification. However, in the practical RFID indoor localization application scenarios, the RFID readers need to keep working for an extensive time. When some readers malfunction and fail to be repaired in time, the existing algorithms usually cannot maintain the accuracy of the original localization system. In this paper, we propose an RFID reader-fault-adaptive localization algorithm based on online sequential fuzzy broad learning system, called RF-OSFBLS algorithm. The RF-OSFBLS algorithm improves the fuzzy broad learning system (FBLS) with the ability of online sequential learning (OSFBLS) by using the updating algorithm, so that it can process data streams that continue to arrive in the environment. Meanwhile, RF-OSFBLS algorithm proposes RFID reader-fault-adaptive strategy by introducing a transformation matrix, which can process subsequent data streams when reader fault occurs. We have carried out experiments to study the influence factors and validate the performance, both the simulation and realistic experiment results show that our proposed RF-OSFBLS algorithm can achieve better positioning effect and maintain a relatively high accuracy in the dynamically changing and reader-fault environment. (C) 2020 Elsevier B.V. All rights reserved.