This article presents a comparison between the read range of classical radio frequency identification (RFID) and chipless RFID in free-space and real environments (REs). From the presented model, read range is derived using analytical formulation over nonisolated channels for both technologies. This article shows that the well-known bounds classically used in free space cannot be achieved in REs, i.e., subjected to leakage, coupling, and/or reflections) and therefore need to be modified. Although the introduced bound for semipassive tags is close to the one corresponding to free space, however, we show that for chipless tags, read range cannot be predicted by the radar equation and presents severe limitations since these tags are linear time-invariant systems. This new bound does not depend on the transmitted power or the reader sensitivity but relies on the concept of residual environment. We show that this quantity is the key parameter to evaluate the read range of a chipless RFID system. Moreover, the residual environment can easily be estimated or measured for any reading system. We show that the proposed bound can accurately predict the read range of a chipless system in different environments from anechoic chamber to nonstationary REs. Results are confirmed in simulation and measurements where the difference between the theoretical read range and the measured one is only 20 cm.

A temperature and humidity chipless RFID sensor is presented in this paper. The tag ID as well as the two physical parameters are extracted using a traditional chipless RFID tag and interrogation procedure. No specific material, i.e. particularly sensitive to the quantities to be measured, is used for ease of implementation. The method is based on a model which describes precisely the evolution of the scatterer’s resonance frequency as a function of temperature and humidity. The materials permittivity (impacted by these two quantities) and dilatation (impacted only by the temperature) are taken into consideration to estimate these two physical quantities independently and simultaneously using only the measurement of the resonance frequencies of two scatterers. Measurements in a controlled environment and in a real environment are achieved showing the potential of this approach for the sensor application. The sensing limitation as well as the possibility of this approach to characterize the temperature and humidity dependencies of unknown materials are also presented.

Differential RCS characterizes the aptitude of a tag to modulate the backscattered power. This parameter is classically estimated based on the variation of the IQ channels in the time domain. This paper introduces a generalization of the RCS backscattered by a tag and a new definition of the delta RCS in the frequency domain. The analytical model is based on schochastic processes and allows to estimate the delta RCS from the power spectral density of the modulated signal backscattered by the tag. The associated measurement is compared to the classical time domain based methods. Results shows a good agreement with a difference between the different approaches less than 0.5 dB. Finally, the proposed method relies on simple synchronization between the emitter and receiver to estimate the delta RCS while maintaining the accuracy.

In this paper, a novel method for the extraction of aspect-independent parameters (that are analogous to complex natural resonances) of chipless radio frequency (RF) identification (RFID) tags is presented. This method is based on short-time Fourier transform (STFT). The concept is proved by utilizing classical depolarizing tags. Owing to the proposed spectrogram approach, the reading success rate of chipless RFID tag is improved by accurate extraction of identification (ID) based on the extraction of quality factors. With single tag measurement (i.e., without empty measurement) in a practical environment, the performance of the proposed spectrogram method is characterized: 1) at various misalignments of the reader and the tag. 2) at various distances for the tag mounted on numerous objects. 3) at various displacements within 3D zone of reader interrogation. The proposed technique is computationally fast due to the inherent nature of fast Fourier transform (FFT) based STFT.

In this article, a millimeter-wave (mmWave) chipless radio-frequency identification (RFID) tag is developed to operate in the V-band for authentication applications. A novel approach based on tag backscattered E-field measurements at different orientation angles for unitary classification is proposed. The concept is based on the hardness to identically reproduce materials due to the inherent randomness in the fabrication process. These uncertainties are transcribed in very small variations that can be observed in the tag electromagnetic response. A set of 16 identical tags were fabricated, and each tag shares the same fabrication mask and manufacturing process method. Spatial diversity using the tag backscattering pattern (at two different angles) adds independent characteristics for estimating authenticity of each tag. To better exploit the large amount of data collected with this approach, a machine learning (ML) sighting classification is used, which enhances the system performance. The probability of error (PE) achieved with the method is around 1%. This PE is four times lower than the one obtained with a similar approach implemented in the X-band.

In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss.