Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals

Abstract

State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy.

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

“…These approaches have their own advantages and limitations. Increasing numbers of studies focus on an automated and accurate fingerprinting method for indoor positioning and navigation systems [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This section summarizes the main localization methods realized by users or by robots and presents a discussion of these methods.…”

“…In the recent past, mobile robot indoor positioning has been the focus of multiple activities that have aimed to optimize time and manpower significantly compared to manual position determination [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. The work in [ 34 ] studies two parameters that might affect measurement quality: the WiFi Access Point (AP) antenna height and the WiFi AP–Receiver distance separation.…”

“…Recently, in [ 42 ], a new dual-frequency Phase Difference of Arrival (PDoA)-based indoor localization system using a mobile robot was proposed. The experiment was carried out in a simulated two-dimensional 25 m 2 area.…”

“…Regarding the system complexity, only one RFID tag is used for the environment calibration and four are used for position estimation, hence reducing the system’s complexity and cost. In order to assess our approach, Section 4 compares our method with the systems presented in [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ].…”

“…The localization accuracy achieved a sub-metric order with a large number of RPs. The solutions in [ 34 , 35 , 36 , 38 , 39 , 40 , 41 , 42 ] are based on developing algorithms to improve the localization system performance without taking into consideration the robot’s displacement errors and the system’s complexity. Hence, the proposed system focused more on optimizing the robot’s displacement errors, which may accumulate over long paths indoor, while maintaining a low level of needed hardware.…”