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.

Thermal modeling of different transmission lines (TLs) based on resonant scatterers is presented. The coplanar strip (CPS) and microstrip (MS) TLs are used to model resonators and to introduce the thermal dependence. A reflectometry approach is employed to validate the model by detecting the scatterers’ resonance frequency and comparing it with analytical expressions. The observable shift in the resonance frequency of the scatterers with temperature variations is due to the thermal expansion of metals and temperature dependence of the substrate permittivity. Since all the measurements are done remotely with no direct line of sight, it is shown how such a reflectometry approach can be used for remote temperature sensing using a passive label composed of resonators. Unlike previous works in this domain where the thermal dependence is considered empirically, the introduced model is used to take into account all thermal effects affecting the resonant scatterers allowing to link rigorously the variations of the measured resonance frequency with the temperature without any lookup table. Temperature sensing using very simple TLs based on resonant scatterers was demonstrated in a real environment. A temperature error of less than 3 °C is obtained. Once the temperature has been determined, it is possible to go back to the TL parameters, such as the effective permittivity and the physical length.

This paper discusses a bistatic reading configuration that employs a frequency-spatial encoding strategy to enhance the data encoding capacity in chipless radio frequency identification (RFID) systems. The proposed method allows for the estimation of resonant frequencies associated with each scatterer and the determination of relative resonator positions. The approach relies on an imaging algorithm that evaluates the positions of the resonators by considering a single-frequency point corresponding to the resonant frequency of each scatterer. Consequently, the proposed method does not seek to image the shape of the metal scatterers but rather to locate their positions geometrically for different frequencies. Indeed, once the resonance frequencies have been detected, a geometric representation of the backscattered field is created by limiting the observable frequency band to the resonance frequencies. The use of individual processing with single-frequency steps enables the distinction of the side-by-side gap between resonators, even at resolutions below the conventional image resolution. Furthermore, this method has the capability to differentiate the gap spacing, which can be as small as λ/8, between resonators operating at different resonance frequencies. This technique significantly increases the coding capacity associated with the chipless tag. A proof of concept (PoC) is evaluated based on loop resonators for identification purposes. Then, anechoic and real scenario measurements (3D displacement table and conveyor belt) are considered in different acquisition configurations. An office environment reading range of up to 20cm has been reported.