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) sensorfusion 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 (SAR) 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 analysed 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 micro-class 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 alongside multiple trajectories. By properly combining the phase data associated to a set of multiple paths, the synthetic apertures along the main directions enlarge 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 sources of measurement uncertainty. 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 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).

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