# Chipless RFID Tag Imaging System based on Conveyor-Belt Radio Frequency Scanning

Raymundo Amorim, Nicolas Barbot, Romain Siragusa, Etienne Perret

► To cite this version:

Raymundo Amorim, Nicolas Barbot, Romain Siragusa, Etienne Perret. Chipless RFID Tag Imaging System based on Conveyor-Belt Radio Frequency Scanning. 2023 IEEE Conference on Antenna Measurements and Applications (CAMA), Nov 2023, Genoa, Italy. pp.636-639, 10.1109/CAMA57522.2023.10352702. hal-04853362

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# Chipless RFID Tag Imaging System based on Conveyor-Belt Radio Frequency Scanning

Raymundo Amorim
LCIS, Grenoble-INP
Univ. Grenoble Alpes
Valence, France

Nicolas Barbot
LCIS, Grenoble-INP
Univ. Grenoble Alpes
Valence, France

Romain Siragusa
LCIS, Grenoble-INP
Univ. Grenoble Alpes
Valence, France

Etienne Perret
LCIS, Grenoble-INP
Univ. Grenoble Alpes
Valence, France

***Abstract***—This paper addresses a bistatic cross-polarized reading configuration for a frequency-spatial encoding strategy to enhance the data encoding capacity in chipless RFID systems. The proposed method can distinguish the geometrical position of each resonator by selecting its resonance frequency using the back-projection algorithm. The application principle consists of collecting measurements during the displacement of a tag attached to an object moving on a conveyor. The measurement system uses this movement to realize the RF image and read the tag ID. The cross-polarized antennas are placed in the same position, which reduces the tag-to-antennas positioning uncertainty. The resonators are distributed considering a surface equivalent to a credit card size, and the resonators' positions are evaluated considering a single frequency corresponding to each resonator's frequency.

***Index Terms***—Imaging, resonant elements, back-projection, identification, and chipless RFID tags.

## I. INTRODUCTION

Radar systems' scalability includes cost, power consumption, and computational cost, these aspects drive the need for innovative, cost-effective, and energy-efficient onboard device solutions. Furthermore, practical systems design attempts to optimize the trade-off between radar target distinctiveness and the system's complexity. In this context, the radar system cost is a function of hardware complexity, which has a direct impact on both target identification and the ability to scale the system effectively. As a prominent solution that gathers low cost and simplicity, chipless RFID technology is a potential technology, which provides a non-invasive method where an electromagnetic signature distinction is based on the information that can be read directly from the backscatter signal representation. Chipless RFID tag systems do not need a direct line of sight and can be fully printable in bio-sourced materials by low-cost methods. Generally, chipless tags can be seen as a radar target designed to scatter a specific electromagnetic signature [1].

Generally, chipless tags are detectable by any UWB radar system, this capability makes chipless RFID technology highly versatile and suitable for a wide range of applications. Given the ability of chipless tags to be detected by any UWB (Ultra-Wideband) radar system, their versatility opens up a wide range of potential applications, including imaging systems. In this context, conventional imaging systems rely on an aperture size associated with a small transmit wavelength to realize adequate resolution to distinguish an object while relying on

a large frequency bandwidth to differentiate objects in depth [2]–[4]. To resolve two objects in range direction, at least 30 GHz bandwidth must be employed to achieve a precision of a few millimeters. To reduce time acquisition and enable hardware simplification some single-frequency point imaging techniques are proposed [5]–[7], which is related to the capacity of the cross-range distinction. The single-frequency simplifies the data acquisition process by enhancing the data speed acquisition and simplifying the hardware required for imaging applications. However, range resolution information became meaningless. Therefore, by avoiding wide spectral bandwidth, the single-frequency imaging system simplifies RF hardware requirements compared to conventional imaging systems. Furthermore, considering chipless applications, the resonators usually are organized considering 2D arrangement, which matches with single-frequency point imaging framework.

Most reported chipless RFID image-based applications rely on resonant scatterers as reflective targets [8]–[13]. Furthermore, the traditional encoding information over polarimetry, spatial location, or phase is employed. As conventional applications, these chipless application attempts to resolve the tag shape by using a resolution compatible with the tag size. Different from conventional chipless approaches, we propose frequency-spatial encoding information, adding two independent complementary information. Therefore, a resonator can be distinguished by its resonance frequency and, additionally, its spatial position.

This paper proposes a method for imaging a chipless RFID tag placed on an object that moves along a conveyor belt. Static measurements are performed on the conveyor belt as a simplified case of real implementation. The natural conveyor belt movement may favor the measurements along the movement direction. The idea is to exploit this movement to create a reading system based on RF imaging for chipless RFID resonant targets.

## II. CHIPLESS RFID TAG IMAGING WORKING PRINCIPLE

This approach relies on an imaging algorithm that assesses the positions of resonators at a single frequency point. In fact, this is a different approach to those that usually involve using measurements obtained at several frequencies to produce the image. Consequently, the main objective is not to characterize
<figure><img src="image_2.png" /><figcaption>Fig. 1: Geometrical characteristics and parameters considering chipless RFID multiple-dipoles resonators for single-frequency image system.</figcaption></figure>

the shapes of the resonators by computing a classical image, but rather to accurately identify their locations associated with their resonance frequencies, which is possible by using a very limited number of frequencies (one per resonance frequency of our resonators). Instead of using a wide broadband signal, each resonance frequency is exploited to distinguish through a spatial image representation considering a cartesian plan. Thus, a single resonance frequency point is considered for imaging formation considering single and multiple resonators.

### A. Simulation of conveyor belt configuration

To illustrate the single frequency for the spatial-frequency approach, we consider a bistatic setup that is represented by a linearly polarized plane wave and a set of cross-polarized farfield probes. Considering the geometry presented in Fig. 1, the retrieved data corresponding to the signal transmitted by the plane wave and backscattered by the tag is received by the set of cross-polarized probes along the synthetic aperture ($L$).

For a first proof-of-concept, a tag composed of multiple dipoles with the same length backed by a ground plane has been simulated. The dipoles have the same length ($l = 20$ mm, a gap ($g = 0.5$ mm), width trace ($w = 2$ mm), metal thickness ($t = 0.05$ mm), and rotated $\alpha = 45^\circ$. The resonator is designed on Rogers 4003C substrate with relative permittivity $\epsilon_r = 3.55$ and loss tangent $\tan \delta = 0.0027$ with a substrate thickness 0.81 mm. A substrate of dimensions (25 mm x 25 mm) has been considered. The resonator is shifted 50 mm in x-axis direction and is located at a distance of $R_0 = 50$ cm from the probes.

It is important to highlight that the cross-polarization configuration enables isolating the tag from non-resonant contributions, which in practice reduces the clutter environment contributions. Further, from an application point of view, the configuration illustrated in Fig. 1 represents the tag movement along a conveyor belt for one tag position, where each probe position corresponds to a measurement performed by one antenna along the linear translation movement on a conveyor belt.

### B. Backscattered signals

The resonator's backscattered E-field magnitudes were obtained by electromagnetic simulation using CST Microwave

<figure><img src="image_3.png" /><figcaption>Fig. 2: E-field cross-polarized frequency response from a multiple-dipoles resonator (a) normalized $|E|$ amplitudes level and (b) $\angle E$ phase amplitudes along the synthetic aperture $L$.</figcaption></figure>

Studio. The received backscattered simulated data considering 51 probes are shown in Fig. 2. The normalized magnitudes ($|E|$) and phases ($\angle E$) along the synthetic aperture are shown in Fig 2(a) and Fig 2(b), respectively. A resonance frequency of $f_r = 3.63$ GHz is determined by analyzing the amplitude signal from at least one of the probe. It is essential to highlight that the resonance is retrieved along the aperture path, which relies on a constant frequency along the synthetic aperture (see Fig. 2(b)). The phase of each probe varies since the distance between the tag and probe is different for the received round-trip signals.

Note that the probes in the extremities of the synthetic aperture have lower amplitude peaks when compared to the probes located in front of the tag. In a practical situation, measurements outside of the field of view (FoV) of the antenna, regions where the amplitude of the backscattered signal is too low at the resonance frequency, do not collaborate with the image formation.

### C. Single-frequency imaging post-processing

Once the data is collected and the resonance frequency is identified ($f_r$) along the synthetic aperture, the back-projection algorithm is evaluated to obtain an image in a chosen distance plan ($R_0 = 50$ cm). Since the retrieved data correspond only to the x-axis probe variation, the y-direction is unresolved. Thus, the resonator can not be distinguished along the y-axis as depicted in Fig. 3(a). The y-axis information is worthless, then the information is represented in a 1D plot. Therefore, the $y = 0$ is chosen to plot the results in a cross-section (CS) amplitude graph (Fig. 3(b)). The amplitude peak of the
<figure><img src="image_4.png" /><figcaption>Fig. 3: (a) Image formation according to the resonance frequency ($f_r$ = 3.63 GHz) and (b) simulated CS amplitude as a function of synthetic aperture.</figcaption></figure>

CS graph corresponds to the resonator position. The actual position of the multiple-dipoles resonator matches precisely with the CS amplitude peak. Next, static measurements on a conveyor belt considering a tag with 8 resonators are evaluated.

### III. CONVEYOR BELT MEASUREMENT SYSTEM

To assess the effectiveness of the proposed approach the measurements were carried out on a conveyor belt in a high multi-path environment. We consider a bistatic measurement setup with one transmitter antenna (Tx) and one receiving antenna (Rx) as shown in Fig. 4. The cross-polarized antenna (QH2000) is placed at the same position as shown in Fig. 4 and operates from 2 GHz – 8 GHz frequency bandwidth. The monostatic cross-polarization configuration favors the reduction of clutter contributions.

To emphasize the spatial-frequency approach, a well-known tag with eight multiple-dipoles resonators ($R_1, ..., R_8$) with different lengths was measured [14], occupying the total size of a credit card as shown in Fig 4. Hence, the 8-resonant elements have different resonance frequencies. Note that the resonant elements are distributed according to 2D spatial arrangement. However, due to the nature of the acquisition, the resolution following the y-axis is unresolved. It is important to noteworthy that it does not limit the approach, since two resonators can be distinguished even when they share the same position according to the y-axis. Therefore, the spatial-frequency identification approach allows the localization of resonators arranged in 2D configuration (as the one shown in Fig. 4) even when there is only a 1D displacement observed, which cannot be observed on conventional imaging systems. Nevertheless, when two resonators share the same resonance frequency and occupy the same location, it is not possible to distinguish them.

A measurement when no object is present in front of the antenna is performed. Then, the $S_{21}$ parameter is measured for each position of the tag, and the background subtraction is applied.

<figure><img src="image_5.png" /><figcaption>Fig. 4: Conveyor belt mechanical system with the parameter to tag imaging evaluation.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5: $|S_{21}|$ cross-polarized response from the 8-multiple-dipoles. (a) Amplitude responses as function of frequency (b) normalized amplitudes along the synthetic aperture.</figcaption></figure>

#### A. Static measurements

To evaluate the method's effectiveness, the 8-multiple-dipoles tag has been measured in a real environment. A simplified case where the tag is measured in a static configuration is evaluated. Therefore, the tag is placed in 51 fixed positions equally spaced along a synthetic aperture $L = 60$ cm, thus the tag measurements are recorded at each step. Note this simplified measurement setup focuses on showing the ideal case where the positions of the tag are known and establishes the sensibility required in a multi-path and high-reflective environment.

The $|S_{21}|$ amplitudes considering the synthetic aperture is given in Fig. 5. As we can see in Fig. 5(a), the prominent peaks that correspond to the resonance frequency of each resonator indicate a suitable backscattered signal from $R_1$ to $R_6$. It is noteworthy that even when cross-polarization is employed, the two last resonances $R_7$ and $R_8$ have a low signal-to-noise level, thus limiting the maximum reading range to $R_0 \le 20$ cm. Also, the FoV is limited to 30 cm as seen in Fig. 5(b). Then, measurements outside of this spatial window do not collaborate with the image formation.
<figure><img src="image_7.png" /><figcaption>Fig. 6: Cross-section amplitudes considering each resonator resonance frequency.</figcaption></figure>

## B. Post-processing imaging evaluation

The BP algorithm is evaluated according to each resonance frequency, which yields the CS amplitudes depicted in Fig. 6. The CS amplitudes are extracted from the image considering the $y = 0$ position, which relates to a 1D graph. According to each resonator's resonance frequency, their position is retrieved by considering the maximum CS amplitude. As depicted in Fig. 6, the $R_7$ and $R_8$ show a slight shift around their real position. An error $\pm 0.18$ cm is noticed. Therefore, a margin of error $\pm 0.18$ cm is considered around the actual position. Then, the change to an industrial environment does not negatively impact the method performance and paves the way to an implementation considering a high multi-path environment. The geometric position of the scatterers can be found with their specific resonance frequency considering only a linear movement. It represents a simplification in terms of hardware and time of acquisition. Smaller resonators can be used to allow more elements in the same area. However, some manufacturing constraints and the coupling among the elements limit these designs mainly in high frequencies.

## IV. CONCLUSION

A method using a spatial-frequency strategy for chipless tag resonator identification has been experimentally validated through numerical models and experiments. In addition, the proof-of-concept using multiple-dipoles cross-polarized resonators has been demonstrated. First, a tag based on one 8-multiple-dipoles was simulated to evaluate the approach. Then, the cross-polarized data set backscattered field considering conveyor belt acquisition has been simulated. Next, a tag based on 8 multiple-dipole resonators was measured considering static measurement in a multi-path environment. Finally, the resonator's positions are retrieved. Despite the linear movement along one direction, 2D arranged resonator's positions are successfully retrieved. Different from conventional approaches, this technique can provide the distinction of elements arranged in a 2D plan considering 1D displacement.

## V. ACKNOWLEDGEMENT

The authors would like to acknowledge the University Grenoble Alpes for financially supporting this project AUSTRALE via the ANR program. This project has also received

funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (ScattererID - grant agreement N° 772539).

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