Radio Frequency Identification Localization Research Articles

RFID (Radio Frequency Identification) Localization and Application

RFID (Radio Frequency Identification) technology has witnessed widespread adoption across diverse industries and sectors due to its versatility and ability to provide real-time tracking, monitoring, and data capture [...]

Efficacy of the Radiofrequency Identification Technique in Breast Cancer Patients: A Single Institution Retrospective Study.

Breast conserving surgery is an excellent option in the treatment of breast cancer. To achieve a good result with this modality, a surgeon needs to identify and excise the tumor with adequate margins. The radiofrequency identification (RFID) technique is a wireless localization technique used for intraoperative breast lesion identification. We assessed the efficacy and outcomes of the RFID technique in breast cancer patients at our institution. This is a single institution, retrospective study (BSMH 22-02X-MWH) of 73 patients. We analyzed the medical records of women with biopsy-proven breast cancer from June 2020 to August 2022; participants received surgical care at Mercy Health West Hospital. Data collected included demographics, clinicopathological characteristics, and surgical procedure. The primary objective was to determine the safety and efficacy of RFID. The secondary objective was to assess the impact of obesity and breast density on the RFID outcomes. A total of 73 female patients met the eligibility criteria with stage I (59%) and grade 1 (51%) breast cancer with mean age of at diagnosis of 66.8 years and mean body mass index of 31.4 kg/m2. Patients had invasive ductal carcinoma (61%), hormonal positive (56%), and human epidermal growth factor receptor 2 negative (68%) disease. All RFID tags were placed under image guidance with 100% accuracy of placement with no evidence of migration or procedure revision. Ninety percent of patients had free surgical margins and only seven patients needed margin re-excision with successful removal of the lesion and the tag. RFID localization technique is a safe, effective and reliable procedure that results in favorable patient outcomes and quality of life.

RFID trial: localization of non-palpable breast lesions using radiofrequency identification tags or wire

BackgroundBreast cancer is the most frequently diagnosed cancer and the leading cause of cancer death in women. Approximately 50% of breast cancers are discovered at an early stage in patients for whom conservative surgery is indicated. Intraoperative localization of non-palpable breast lesions is generally accomplished using a hook wire to mark the area of concern under ultrasound or stereotactic localization. But this technique has several drawbacks (painful, stressful…). We propose the use of a wire-free breast lesion system using miniature radiofrequency identification (RFID) tags. This technique could improve patient comfort and surgical comfort for surgeons. We therefore propose a study to assess the interest of introducing the RFID localization technique at the Jean PERRIN comprehensive cancer center.MethodsThis is a single-center prospective trial designed to assess the interest in introducing the RFID localization technique at the Jean Perrin center. It aims to show the superiority of the RFID technique in terms of patient tolerance compared to the gold-standard (hook wire). A sequential inclusion in time will be performed: 20 inclusions in the gold-standard group, then 20 patients in the RFID group before repeating the inclusion scheme. Any patient requiring preoperative localization will receive a senology consultation. The RFID tag will be placed during this consultation. The hook wire localization will be done the day before the surgery. Patients will fill out a Hospital Anxiety and Depression scale (HAD) questionnaire at the time of inclusion. They will then fill out a satisfaction questionnaire in 2 steps: during the placement of the device (RFID tag or hook wire) or during the postoperative consultation at 1 month. Radiologists and surgeons will fill out a questionnaire to evaluate the localization technique, respectively after the localization and surgery procedures.DiscussionThe RFID study is the first study in France which specifically assesses the interest of the RFID localization in terms of patients comfort. Patient comfort is one of the key elements to take into consideration when managing patients in oncology and new technologies such as RFID tags could improve it.Trial registrationClinicalTrials.gov ID; NCT04750889 registered on February 11, 2021.

Radiofrequency localization of nonpalpable breast cancer in a multicentre prospective cohort study: feasibility, clinical acceptability, and safety

PurposeIn breast conserving surgery, accurate lesion localization is essential for obtaining adequate surgical margins. Preoperative wire localization (WL) and radioactive seed localization (RSL) are widely accepted methods to guide surgical excision of nonpalpable breast lesions but are limited by logistical challenges, migration issues, and legislative complexities. Radiofrequency identification (RFID) technology may offer a viable alternative. The purpose of this study was to evaluate the feasibility, clinical acceptability, and safety of RFID surgical guidance for localization of nonpalpable breast cancer.MethodsIn a prospective multicentre cohort study, the first 100 RFID localization procedures were included. The primary outcome was the percentage of clear resection margins and re-excision rate. Secondary outcomes included procedure details, user experience, learningcurve, and adverse events.ResultsBetween April 2019 and May 2021, 100 women underwent RFID guided breast conserving surgery. Clear resection margins were obtained in 89 out of 96 included patients (92.7%), re-excision was indicated in three patients (3.1%). Radiologists reported difficulties with the placement of the RFID tag, partially related to the relatively large needle-applicator (12-gauge). This led to the premature termination of the study in the hospital using RSL as regular care. The radiologist experience was improved after a manufacturer modification of the needle-applicator. Surgical localization involved a low learning curve. Adverse events (n = 33) included dislocation of the marker during insertion (8%) and hematomas (9%). The majority of adverse events (85%) occurred using the first-generation needle-applicator.ConclusionRFID technology is a potential alternative for non-radioactive and non-wire localization of nonpalpable breast lesions.

DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID

Passive radio frequency identification (RFID) tags have been widely used in logistics, supply chain, warehousing, and other fields. However, for RFID-based automatic inventory management in warehouses, the deployment of current methods is more complex, and the localization range still has some limitations. This paper proposes DNCL, which is a hybrid passive RFID localization scheme based on direction-of-arrival (DOA) estimation and nonmetric multidimensional scaling (NMDS) to achieve spatial coordinate localization of tagged objects on shelves or racks using a single antenna for simple 1D scanning. DNCL uses antenna dynamic scanning to generate a virtual antenna array for the dynamic information capture of tags in the scene, which helps eliminate the phase shift produced by ambient noise. We apply the angle profile linear model to identify the characteristics of each tag and introduce the NMDS algorithm to improve the robustness of the scheme through the fixed layout of the reference tags, which can reliably estimate the coordinates of the tagged objects in the space. This paper realizes a prototype system and validated its practical performance in real complex situationse by COTS RFID devices. The results indicate that DNCL can achieve high accuracy for the localization of passive tags in free space.

A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System

The misreading problem of a passive ultra-high-frequency (UHF) radio frequency identification (RFID) tag is a frequent problem arising in the field of librarianship. Unfortunately, existing solutions are something inefficient, e.g., extra resource requirement, inaccuracy, and empiricism. To this end, under comprehensive analysis on the passive UHF RFID application in the librarianship scenario, a novel and judicious approach based on RFID localization is proposed to address such a misreading problem. Extensive simulation results show that the proposed approach can outperform the existing ones and can be an attractive candidate in practice.

RFID-Based Vehicle Localization Using Virtual Wideband Multi-Frequency Continuous Wave

Vehicular location information plays a crucial role in intelligent transportation systems. A particular focus is on developing an internet of things (IoT)-based sensing system for smart roads to enable high-precision vehicle localization that does not rely on global navigation satellite system (GNSS) and improve traffic safety and efficiency. As a passive low-power IoT technology, radio frequency identification (RFID) has been proposed as an alternative localization approach in GNSS-denied scenarios. However, limited by the licensed narrow bandwidth, the localization accuracy of commercial off-the-shelf (COTS) RFID readers is quite low. To achieve the RFID-based high-precision localization, we develop an RFID localization reader on a universal software radio peripheral (USRP) platform, which realizes virtual broadband multi-frequency continuous-wave (MFCW) transmission and phase extraction of the received backscattered signals. The genetic algorithm (GA) is applied to choose the optimal frequency set to minimize the range difference estimating error. By calculating the phase difference of arrival (PDOA), the localization problem can be modeled as hyperbolic equations and solved. Experimental results validate that our developed RFID-based vehicle localization system could achieve an accuracy less than 5 cm.

RFID Landslide Monitoring: Long-Term Outdoor Signal Processing and Phase Unwrapping

Localization of passive Radio-Frequency Identification (RFID) tags has been used to monitor landslide surface displacement since 5 years. This method, applied on slow displacements lower than 1cm per day, allows a high spatio-temporal resolution at a relatively low cost. With the feedback of the previous years, this paper proposes to summarize the various challenges encountered with the long-term outdoor RFID localization method, and presents data-processing solutions that were implemented to overcome these challenges. We propose a complex-smoothing unwrapping algorithm, a multi-frequency merging operation, as well as multi-tag and multi-antenna phase combining method. The concept of an unwrapping reference guide is presented and applied with groups of tags showing coherent displacements, or with absolute reference measurements. These approaches allow a higher data availability up to 38% for one site over multiple years, and a better phase unwrapping. Earth surface displacement monitoring with RFID proves to be a robust and accurate solution, with four equipped sites across France and Switzerland.

Application of Internet of Things Combined with Wireless Network Technology in Volleyball Teaching and Training.

Motion information collection technology is a means for measuring, tracking, and recording the movement traces of individuals in space. This method can complete the data collection of volleyball players and the ball trajectory and realize quantification and statistical analysis of the data to present a virtual model of the player's movement trajectory. It is inseparable from the acquisition of information to complete the information collection. Therefore, this work uses the radio frequency identification (RFID) technology in the Internet of Things technology to build an information collection system and apply it to volleyball sports. The existing positioning system based on RFID has problems such as significant positioning errors and high system costs due to the arrangement of a large number of readers. This paper first introduces the theoretical knowledge of the RFID system, wireless network positioning technology, and RFID system positioning method in the Internet of Things. Besides, the theoretical framework of the volleyball movement information acquisition system is presented based on the Received Signal Strength Indication of the RFID system and Location Identification based on the Dynamic Active RFID Calibration (LANDMARC) algorithm. Then, the LANDMARC algorithm is improved through the Centroid Positioning algorithm, forming the CP-LANDMARC algorithm. Finally, a simulation experiment is conducted to test the system effect. The results demonstrate that: (1) the average error of the basic LANDMARC algorithm is 0.55 meters, and the average error of the CP-LANDMARC algorithm is 0.46 meters; (2) the average error of the CP-LANDMARC algorithm is 0.43 meters when the reference label is set to be evenly distributed in a square, and the average error of the optimized algorithm is 0.38 m when the reference label is set as an equilateral triangle; and (3) when the number of reference labels increases to 110, the average error decreases from 0.38 to 0.29. This paper aims to improve the quality of volleyball teaching and training by designing a relevant sports information acquisition system.

2D Phase-Based RFID Localization for On-Site Landslide Monitoring

Passive radio-frequency identification (RFID) was recently used to monitor landslide displacement at a high spatio-temporal resolution but only measured 1D displacement. This study demonstrates the tracking of 2D displacements, using an array of antennas connected to an RFID interrogator. Ten tags were deployed on a landslide for 12 months and 2D relative localization was performed using a phase-of-arrival approach. A period of landslide activity was monitored through RFID and displacements were confirmed by reference measurements. The tags showed displacements of up to 1.2 m over the monitored period. The centimeter-scale accuracy of the technique was confirmed experimentally and theoretically for horizontal localization by developing a measurement model that included antenna and tag positions, as well as multipath interference. This study confirms that 2D landslide displacement tracking with RFID is feasible at relatively low instrumental and maintenance cost.

Radio Frequency Identification target location based on the Unmanned Aerial Vehicle

Due to the complex environment, the traditional Radio Frequency Identification (RFID) localization algorithm is cumbersome, the system localization accuracy is low, and the calculation time is relatively complex, an RFID localization algorithm based on sequential Kalman filtering has been proposed in this paper. Through the flight of sequential points, The Unmanned Aerial Vehicle (UAV) with the reader collects the Received Signal Strength Indication (RSSI) data dealt with mixed filtering of the target in the scene, after obtaining the coordinates according to the three-ball intersection method, and then using the Unscented Kalman Filter (UKF) algorithm to generate the estimation equation, thereby determining the position trajectory information to estimate. The results show that compared with other positioning methods the system has higher mobility and improved positioning accuracy with an average of 13.15%. At the same time, due to the real-time and flexibility of drones, this system can also be applied to more diverse scenarios. And the algorithm operation time is 1.15 s and it is suitable for UAVs scenarios.

A multicenter prospective cohort study to evaluate feasibility of radio-frequency identification surgical guidance for nonpalpable breast lesions: design and rationale of the RFID Localizer 1 Trial

BackgroundBreast cancer screening and improving imaging techniques have led to an increase in the detection rate of early, nonpalpable breast cancers. For early breast cancer, breast conserving surgery is an effective and safe treatment. Accurate intraoperative lesion localization during breast conserving surgery is essential for adequate surgical margins while sparing surrounding healthy tissue to achieve optimal cosmesis. Preoperative wire localization and radioactive seed localization are accepted standard methods to guide surgical excision of nonpalpable breast lesions. However, these techniques present significant limitations. Radiofrequency identification (RFID) technology offers a new, nonradioactive method for localizing nonpalpable breast lesions in patients undergoing breast conserving surgery. This study aims to evaluate the feasibility of RFID surgical guidance for nonpalpable breast lesions.MethodsThis multicenter prospective cohort study was approved by the Institutional Review Board of the University Medical Center Utrecht. Written informed consent is obtained from all participants. Women with nonpalpable, histologically proven in situ or invasive breast cancer, who can undergo breast conserving surgery with RFID localization are considered eligible for participation. An RFID tag is placed under ultrasound guidance, up to 30 days preoperatively. The surgeon localizes the RFID tag with a radiofrequency reader that provides audible and visual real-time surgical guidance.The primary study outcome is the percentage of irradical excisions and reexcision rate, which will be compared to standards of the National Breast Cancer Organisation Netherlands (NABON)(≤ 15% irradical excisions of invasive carcinomas). Secondary outcomes include user acceptability/experiences, learning curve, duration and ease of the placement- and surgical procedure and adverse events.DiscussionThis study evaluates the feasibility of RFID surgical guidance for nonpalpable breast lesions. Results may have implications for the future localization techniques in women with nonpalpable breast cancer undergoing breast conserving surgery.Trial registrationNetherlands National Trial Register, NL8019, registered on September 12th 2019.

An Efficient Method for Hyperbolic-Based Localization in SAR RFID Systems

Radio frequency identification (RFID) is one of the major wireless communication technologies and has been applied in the supply chain and retail network for a long time. By contrast, the application of RFID in localization emerges in recent years. Besides, neither precision nor real time of existing RFID localization methods is satisfactory enough. In this article, we propose a novel method called HyLoc for localization of RFID tags based on the hyperbolic model. The problem to locate RFID tags is modeled as the solution of hyperbolic equations formed by synthetic aperture radar (SAR). To solve the equations quickly, the linear least-squares (LLS) algorithm is first used to find the approximate solution, and then, the estimation error is corrected by the Taylor series method for higher accuracy. Comparisons of performance with the state-of-the-art methods in SAR RFID localization are carried out through simulations and experiments. The results show that the proposed method is closer to the Cramér–Rao lower bound than the holography method at high noise level and more efficient than the ordinary nonlinear least-squares (NLS) method without loss of accuracy.

Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals.

State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy.

A Synthetic Aperture UHF RFID Localization Method by Phase Unwrapping and Hyperbolic Intersection

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. <i>Note to Practitioners</i>&#x2014;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.

Crucial or Unnecessary? Analysis on Phase Differential in Holographic SAR RFID Localization

The development of synthetic aperture radar (SAR) radio frequency identification (RFID) localization technology has taken the RFID based indoor localization research to a new level, among which the holographic localization is the most typical approach. Holographic SAR RFID usually performs phase differential to deal with the problem of unknown initial phase. However, problems with regard to the phase differential have emerged in prior literature, some of which make the localization system perform extra but useless efforts, some affect the comprehensibility of the algorithm, and some even reduce the reliability of localization results. Therefore, the analysis of phase differential is very helpful for researchers and practitioners to complete and improve their work. In this paper, several conclusions concerning the phase differential are drawn and analyzed, which are verified by real experiments.

ReLoc 2.0: UHF-RFID Relative Localization for Drone-Based Inventory Management

Radio frequency identification (RFID) technology attracts extensive attention for industrial applications, especially supply chain businesses and inventory management. In this article, a passive RFID localization scheme based on unmanned aerial vehicles (UAVs) or drones is established for inventory management in warehouses. In case of the major challenge of the on-board antenna tracking errors in practical scenarios, the state-of-the-art (SOTA) numerical methods rely on accurate antenna positions, such as synthetic aperture radar (SAR)-based algorithms, which will introduce large positioning errors and become unreliable. To this end, we propose a new relative RFID localization method based on phase, received signal strength indicator (RSSI), and readability, which is little affected by antenna tracking errors. In the proposed method, the tagged items on the racks are located laterally through pinpointing the minimum of the unwrapped phases, which have been unwrapped based on the hybrid random forest (RF) model. RSSI differences and the read count of each patch antenna’s beam are utilized to distinguish the rack level of tagged assets via a boosting tree classifier. The experimental results in a real warehouse show that the proposed method achieves 27.1-cm mean lateral positioning accuracy and orders the tagged items horizontally and vertically with more than 96.7% and 98.0% accuracy, respectively.

Deep Convolutional Neural Network for Passive RFID Tag Localization Via Joint RSSI and PDOA Fingerprint Features

Radio-frequency identification (RFID) localization has drawn much attention with the emergence of the Internet of Things (IoT). Deep learning with applications to RFID localization owns a lot of advantages. In this paper, we present a deep convolutional neural network (CNN)-based approach for passive RFID tag localization exploiting joint fingerprint features of the received signal strength indication (RSSI) and phase difference of arrival (PDOA). First, the RSSI and PDOA data are extracted from the received signals of RFID readers. Then, a CNN with three convolution layers and pooling layers is design, in which the normalized RSSI and PDOA data are formed into images as its input to train the weights in the offline stage. In the online stage, the RSSI and PDOA data of test tags are collect and then the positions of unknown tags is predected based on the designed CNN. In the simulations, the accuracy of the proposed approach is compared with several fingerprinting-based schemes such as LANDMARC, weighted K-nearest neighbor (WKNN) and deep neural network (DNN), and the impact of different fingerprint data sets and noise variances on the positioning accuracy is analyzed. Experiments show that the proposed approach can locate multiple tags with high accuracy and stability in complex indoor environment, and outperforms other existing schemes.

The Influence of the Nonideal Phase Offset on SAR-Based Localization in Passive UHF RFID

The existing radio frequency identification (RFID) localization methods typically regard the initial phase of the signal as constant. This is feasible when the antenna and the tag are stationary. However, in synthetic aperture radar (SAR) RFID localization, the relative motion of the reader antenna and tag changes the antenna phase center and tag orientation, thus resulting in a continuous change of the initial phase. This change, which will cause the nonideal phase offset (NPO) and, thus, have an adverse effect on positioning, has been neglected in most previous SAR RFID studies. In this article, we investigate the antenna phase uncertainty and the tag orientation-dependent phase offset and analyze their influence on localization accuracy. To the best of our knowledge, this is the first time that the initial phase is regarded as a variable in SAR RFID localization. In addition, a novel motion model-based localization (MoLoc) method is proposed, which exploits the relative motion model and visualizes the influence of NPO more intuitively. Compared with the conventional grid-matching methods, MoLoc shows higher accuracy and much lower computation time. Both the influence of NPO and the performance of MoLoc are verified by experiments implemented in real scenario. The experiments also evaluate the influence of NPO on the grid-matching method. The experimental results show that NPO has a significant impact on the accuracy of SAR-based localization methods.

Performance, Accuracy and Generalization Capability of RFID Tags' Constellation for Indoor Localization.

Indoor localization has recently witnessed an increase in interest due to its wide range of potential services. Further, the location information is very important in many applications, such as the Internet of Things, logistics, library management and so on. Hence, different technologies and techniques have been proposed in the literature for indoor localization systems. Most of these systems present the disadvantages of a poor performance, low accuracy and high cost. However, thanks to its low cost, high accuracy and non-line-of-sight detection, radio frequency identification (RFID)-based localization has increasingly become the most used technology for indoor localization. In this paper, we propose an innovative approach based on the multiple input single output (MISO) protocol to improve the accuracy of a low-cost RFID localization system. Whereas most traditional systems use a single tag for localization, the proposed architecture encourages the use of a group of RFID tags named as a constellation. According to experimental results and based on the signals’ diversity, the location accuracy is improved to get an estimated position error of 81 cm at the cumulative distribution function of 90%.