This paper presents a state-of-the-art analysis on the methods suitable for vehicle indoor localization and exploiting the RFID (Radio Frequency IDentification) technology. The survey describes three main categories of vehicle localization systems: (i) solutions exploiting only the RFID technology, (ii) sensor-fusion techniques combining data from RFID systems and proprioceptive sensors, and (iii) sensor-fusion techniques combing RFID data with those of other exteroceptive sensors in addition to the RFID system itself. For each method, implementation and methodological details are discussed, by highlighting the applied RFID technology, namely passive HF-RFID, passive UHF-RFID, or any other RFID system. Also, the employed RFID parameters, i.e., tag EPC, RSSI or backscattered phase, are discussed. The survey focuses on the achievable localization performance, also accounting for infrastructure-deployment costs together with complexity and maintenance overhead. Positioning, tracking, navigation and simultaneous localization and mapping (SLAM) issues are here considered. The analysis highlights pros and cons of each method, together with the main challenges and perspectives of RFID-based solutions for vehicle localization.

This paper presents and characterizes a measurement method for positioning of passive tags, by a drone equipped with a UHF-RFID reader. The method is based on a synthetic aperture radar approach and exploits the knowledge of the reader/drone trajectory, which is achieved with a differential Global Navigation Satellite System. Different sources of measurement uncertainty are analyzed by means of numerical simulations and experimental results. The method capabilities are discussed versus the length and shape of the reader trajectory. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a microclass unmanned aerial vehicle.

This paper presents the application of the Synthetic Aperture Radar (SAR) localization method for indoor positioning of UHF-RFID tags when the robot-mounted reader antenna moves along multiple trajectories. By properly combining the phase data associated to a set of multiple paths, the whole length of the combined synthetic apertures enlarges and then the localization accuracy may improve. Besides, during consecutive inventory rounds, several tag position estimates are available and they can be profitably combined to minimize the localization uncertainty. Different combination approaches are investigated to determine the best choice to improve the localization performance. The method capabilities are discussed through a numerical analysis, by considering different configurations of the multiple apertures and different measurement uncertainty sources. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a robotic wheeled walker, in an indoor scenario, by employing different types of tags. The knowledge of the reader/robot trajectory required by the SAR method is here achieved with an optical system.

The use of radio frequency identification (RFID) technology for the traceability of products throughout the production chain, warehouse management, and the retail network is spreading in the last years, especially in those industries in line with the concept of Industry 4.0. The last decade has seen the development of increasingly precise and high-performance methods for the localization of goods. This work proposes a reliable 2-D localization methodology that is faster and provides a competitive accuracy, concerning the state-of-the-art techniques. The proposed method leverages a phase-distance model and exploits the synthetic aperture approach and unwrapping techniques for facing phase ambiguity and multipath phenomena. Trilateration applied on consecutive phase readings allows finding hyperbolae as the localization solution space. Analytic calculus is used to compute intersections among the conics that estimate the tag position. An algorithm computes intersections quality to select the best estimation. Experimental tests are conducted to assess the quality of the proposed strategy. A mobile robot equipped with a reader antenna localizes in 2-D the tags placed in an indoor scenario and reconstructs the map of the environment through a simultaneous localization and mapping (SLAM) algorithm. Note to Practitioners —A localization technology based on passive ultrahigh-frequency (UHF) radio frequency identification (RFID) is an enabling technology for intelligent warehouses, logistics, and retails. For this reason, this work presents a novel method to estimate the tag location with high accuracy. A reader antenna is mounted on an autonomous mobile robot that can move in an indoor or outdoor environment due to a simultaneous localization and mapping (SLAM) algorithm. The motion of the antenna generates a synthetic aperture. The system receives the phase measurements from the RFID tags and generates a distance model through phase unwrapping. In such a way, the possible locations of the tags in the environment are generated, creating conics. The trilateration step is performed analytically, intersecting the obtained conics. The resulting estimations are very accurate and not computation expensive. Therefore, the proposed approach can be employed in any application where localizing objects is fundamental even when reduced computational power is available, e.g., in warehouses where the products are at known heights, or where the items are placed on a fixed infrastructure, such as high-shelves logistics, to produce the inventory of the tagged objects within each shelf.

The phase of signals backscattered by Ultra High Frequency (UHF) Radio Frequency Identification (RFID) tags is generally more insensitive to multipath propagation than Received Signal Strength Indicator (RSSI). However, signal phase measurements are inherently ambiguous and could be further affected by unknown phase offsets added by the transponders. As a result, the localisation of an agent by using only signal phase measurements looks infeasible. In this paper, it is shown instead that the design of a dynamic position estimator (e.g. a Kalman filter) based only on signal phase measurement is actually possible. To this end, the necessary conditions to ensure theoretical local nonlinear observability are firstly demonstrated. However, a system that is locally observable guarantees convergence of the localisation algorithm only if the actual initial agent position is approximately known a priori. Therefore, the second part of the analysis covers the global observability, which ensures convergence starting from any initial condition in the state space. It is important to emphasise that complete observability holds only in theory. In fact, measurement uncertainty may greatly affect position estimation convergence. The validity of the analysis and the practicality of this localisation approach are further confirmed by numerical simulations based on an Unscented Kalman Filter (UKF).

This paper presents a particle swarm optimization (PSO) in synthetic aperture radar (SAR) methods to enable real-time 3D localization of UHF-RFID tags. The reader antennas are moved through a moving agent (e.g., unmanned aerial vehicle, unmanned ground vehicle, robot) and they collect several phase samples by resembling a synthetic array. Thanks to the PSO approach the 3D matching function of the SAR method can be calculated in a reduced number of points by keeping an acceptable localization error. A numerical analysis demonstrates the method applicability through a comparison with conventional SAR methods based on the exhaustive search (3D dense grid) of the maximum point. Localization performance is investigated when an agent is equipped with a single antenna moving along a 3D trajectory or with two reader antennas at different height running a planar trajectory. Then, an experimental campaign in indoor scenario with an RFID-equipped unmanned ground vehicle shows the method effectiveness in performing real-time 3D positioning with centimeter-order localization error.