Localization Of Radio Frequency Identification Tags Research Articles

SAW RFID Tag Spatial Division Multiple Access Based on 3D Reflector Response Localization Using a Wideband Holographic Approach

Surface acoustic wave (SAW) radio-frequency identification (RFID) has high potential for industrial applications, where automated identification and localization of assets represent the backbone of process controlling and logistics. However, in situations where multiple tags are simultaneously interrogated, the response patterns corresponding to the hard-coded reflectors are prone to overlap, preventing their association with the corresponding tags and, hence, the correct tag decoding. Identification and localization of multiple SAW RFID tags are addressed in this work under this challenging effect, known as collision, with a multi-antenna mobile robot-based synthetic aperture approach. Using the estimation of the spatial probability density functions of the SAW tag reflectors over a given interrogation aperture, the received impulse responses can be resolved in three dimensions and clustered with respect to their estimated locations. The performance of the proposed approach to associate and localize the signals from multiple tags was evaluated theoretically and experimentally.

Comparison of Wireless Localization Alternatives with Wire Localization for Nonpalpable Breast Lesions.

Radiofrequency identification tag localization (TL) and magnetic seed localization (MSL) are alternatives to wire localization (WL) for excision of nonpalpable breast lesions. We sought to compare localization methods with respect to operative time, specimen volume, and re-excision rate. A retrospective cohort analysis was performed on TL, MSL, and WL lumpectomies and excisional biopsies at a single institution. Association between localization method and operative time, specimen volume, and re-excision rate was assessed by multiple logistic regression using odds ratios (ORs) and 95% CIs. A total of 506 procedures were included: 147 TL (29.0%), 140 MSL (27.7%), and 219 WL (43.3%). On logistic regression analysis, MSL was associated with longer operative times than WL for excisional biopsies only (OR 4.24, 95% CI 1.92 to 9.34, p < 0.001). Mean excisional biopsy time was 39.1 minutes for MSL and 33.0 minutes for WL. Specimen volume did not vary significantly across surgery types between localization methods. In an analysis of all lumpectomies with an indication of carcinoma, marker choice was not associated with rate of re-excision (TL vs WL OR 0.64, 95% CI 0.26 to 1.60, p = 0.342; MSL vs WL OR 1.22, 95% CI 0.60 to 2.49, p = 0.587; TL vs MSL OR 0.65, 95% CI 0.26 to 1.64, p = 0.359). TL, MSL, and WL are comparable in performance for excision of nonpalpable breast lesions. Although increased operative time associated with MSL vs WL excisional biopsies is statistically significant, clinical significance warrants additional study. With similar outcomes, physicians may choose the marker most appropriate for the patient and setting.

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.

Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

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.

A Multitag Cooperative Localization Algorithm Based on Weighted Multidimensional Scaling for Passive UHF RFID

Radio frequency identification (RFID) technology, which is one of the important implementation ways of Internet-of-Things (IoT), has achieved much attention in indoor localization areas. Passive ultrahigh frequency (UHF) RFID tag localization has a great development recently. Most of traditional passive UHF RFID localization algorithms can only achieve the position of one tag at a time while multitag localization is desired in many RFID applications. In this paper, we proposed the weighted multidimensional scaling (WMDS) based on received signal strength (RSS) method and the tag-to-tag communication system to achieve multitag cooperative localization. The targets are marked with passive UHF RFID tags. RSS method is used to determine the distance between readers and target tags through channel models. We can also obtain the estimated distances between target tags by the tag-to-tag communication system. The estimated locations of the tags are determined by the calculation of Euclidean distance matrix through a few iterations. Simulation results show that the WMDS algorithm achieves higher localization accuracy than traditional algorithms and the cooperation between tags improves the localization accuracy.

Radiofrequency identification tag localization is comparable to wire localization for non-palpable breast lesions.

Radiofrequency identification (RFID) tag localization (TL) is a technique of localizing non-palpable breast lesions that can be performed prior to surgery. We sought to evaluate whether TL is comparable to wire localization (WL) in regard to specimen size, operative time, and re-excision rate. A retrospective cohort analysis was performed on TL and WL excisional biopsies and lumpectomies performed by 5 surgeons at 2 institutions. Cases were stratified by surgery type and surgical indication. Associations between localization technique and specimen volume, operative time, and re-excision rate were assessed by univariate and multivariate analyses. A total of 503 procedures were included, 147 TL (29.2%) and 356 WL (70.8%). Nineteen (12.9%) RFID tags were placed before surgery, ranging 1-22days. All intended targets were removed. TL and WL excisional biopsy and lumpectomy specimen volumes were similar (p = 0.560 and 0.494). TL and WL excisional biopsy and lumpectomy + SLNB operative times were similar (p = 0.152 and 0.158), but TL lumpectomies without SLNB took longer than WL (57min vs 49min; p = 0.027). Re-excision rates were similar by surgical procedure (p = 0.615), surgical indication (DCIS p = 0.145; invasive carcinoma p = 0.759), and confirmed by multivariable analysis (OR 0.754, 95% CI 0.392-1.450; p = 0.397). TL has similar surgical outcomes to WL with added benefit that TL can occur prior to the day of surgery. TL is an acceptable alternative to WL and should be considered for non-palpable breast lesions.

Localization of Passive UHF RFID Tags Using the AoAct Transmitter Beamforming Technique

The localization of passive ultra-high frequ- ency (UHF) radio frequency identification (RFID) tags is an emerging technology that has reached a high level of interest in industry and research. Most solutions use classical techniques, such as the angle-of-arrival or the received-signal-strength methods. As an alternative, we present a new approach for estimating the position of a passive UHF RFID tag by using a transmitter beamforming technique that we call angle-of-activation (AoAct). It is based on manipulating the shape of the radiation pattern of the interrogator’s transmitter antenna array. By pivoting the main lobe of the pattern in the azimuth plane of the array and by utilizing the very sensitive response threshold of passive UHF RFID tags, we are able to estimate the tag direction. We use three-channel digital beamforming, where the phases and amplitudes of the antenna array’s feeding radio frequency signals are controlled by the baseband $I$ and $Q$ components of the interrogator’s transmitter signal. Our self-developed hardware pivots the main lobe of a three-element antenna array in a range of ±52° with a resolution of 0.02°. It senses the AoAct with a mean measured accuracy of 1.2° in the anechoic chamber, 1.9° in a basic multipath environment, and 2.5° in a real-world multipath environment. After estimating the AoAct of a tag from different locations, the tag position is triangulated. Experiments show that our AoAct system delivers a localization accuracy of 7 cm in a typical multipath environment.

UHF RFID Localization Based on Synthetic Apertures

Reading ranges are being extended in the wake of recent advances in UHF radio frequency identification (RFID) systems, and with the advent of larger reading ranges, tag localization has moved into the spotlight. Recently, we introduced a new UHF RFID tag localization technique. The proposed method is based on phase measurements taken along a synthetic aperture. A holographic image is calculated based on the scanned phase values. The image represents the spatial probability density function for the actual tag location. This paper presents this innovative method in detail. Simulations that illustrate the effect of the given trajectory are included. Extensive measurements obtained in a reflective lab environment are presented. We discuss the method's effectiveness with respect to measurement errors, antenna phase center distortions, and the available phase information. The results show the potential practical applications for the method in moving reader antennas, such as handheld readers or readers mounted on vehicles like forklifts or mobile robotic systems.

Modified RSSI Technique for the Localization of Passive UHF RFID Tags in LOS Channels

This paper presents a new approach for improving the localization of passive ultra high frequency radio frequency identification (RFID) tags in line-of-sight channels using a received signal strength indicator (RSSI) technique. In practice, the complex propagation in the indoor channels and also the variability of some parameters of the RFID equipment itself introduces significant amount of errors when the operation of localization carries out the RSSI technique. Indeed, as the calculation is based on a trilateration, the incomplete knowledge of the propagation and some parameters of RFID tags leads to estimate distances which are wrong, and therefore the localization cannot be correct. In order to overcome this drawback, the proposed method takes into account the presence of unknown parameters relying on a dichotomous algorithm which includes probabilistic parameters. The presented simulation results are in good agreement with the expected theoretical results. Experimental results show that the proposed method strongly increases the accuracy of the estimated position of tags. Compared to other approaches based on the improvement of the RSSI technique, this method does not require too much complexity in terms of materials (no need for specific architecture or reference tags) and processing (fast and simple algorithm).

Localization of RFID Tags Using Stochastic Tunneling

This paper presents a novel localization scheme in the 3D wireless domain that employs cross correlation in backscattered signal power from a cluster of radio frequency identification (RFID) tags to estimate their location. Spatially co-located RFID tags, energized by a common tag reader, exhibit correlation in their received signal strength indicator (RSSI) values. Hence, for a cluster of RFID tags, the posterior distribution of their unknown radial separation is derived as a function of the measured RSSI correlations between them. The global maxima of this posterior distribution represent the actual radial separation between the RFID tags. The radial separations are then utilized to obtain location estimates of the tags. However, due to the nonconvex nature of the posterior distribution, deterministic optimization methods that are used to solve true radial separations between tags provide inaccurate results due to local maxima, unless the initial radial separation estimates are within the region of attraction of its global maximum. The proposed RFID localization algorithm called LOCalization Using Stochastic Tunneling (LOCUST) utilizes constrained simulated annealing with tunneling transformation to solve this nonconvex posterior distribution. The tunneling transformation allows the optimization search operation to circumvent or tunnel&#x201D; through ill-shaped regions in the posterior distribution resulting in faster convergence to the global maximum. Finally, simulation results of our localization method are presented to demonstrate the theoretical conclusions.

A Novel Communication Range Recognition (CRR) Scheme for Spatial Localization of Passive RFID Tags

RFID (Radio Frequency Identification) technology is expected to be used as a localization tool. By the localization of RFID tags, a mobile robot equipped with an RFID reader can recognize the surrounding environment. In this paper, we propose a novel effective scheme called the communication range recognition (CRR) scheme for localizing RFID tags. In this scheme, an RFID reader determines the boundaries of the communication range when it is appropriately positioned by the robot. We evaluate the estimated position accuracy through numerous experiments. We show that the moving distance of the RFID reader in the proposed scheme is lower than that in conventional schemes.

Radiofrequency Identification Tags for Preoperative Tumor Localization: Proof of Concept

The objective of our study was to experimentally explore the potential for tumor localization using radiofrequency identification (RFID) tags and a newly developed handheld RFID detector. A unique RFID detector that combines the use of multiple interchangeable detector probes with both audio and LCD display signals was invented, allowing precise localization and identification of RFID tags. Accurate localization and identification were validated using this handheld RFID detector (TagFinder) and RFID tags of 2-mm diameter and 8- or 12-mm lengths. Experiments included the following: validation in various breast phantoms; differentiation of 4- to 6-cm-diameter tissue specimens with and without tags; determination of the nearest differentiable distance between two tags; proof of visualization of tags on sonography, radiography, and MRI; and experimental localization and resection of RFID-labeled tissue specimens. Both 8- and 12-mm-length RFID tags implanted < 6 cm deep were accurately localized and uniquely identified. Chicken breast specimens of 4- to 6-cm diameter implanted with RFID tags were accurately differentiated from specimens without tags. Tags in proximity could be reliably differentiated and uniquely identified when placed as close as 0-2 cm apart, depending on the tags' precise orientations. RFID tags were easily visualized with sonography, mammography, and MRI, with artifacts present only on MRI. Localization and resection of RFID tags in the labeled tissue region were successful in grocery store-bought chicken breasts. The combination of RFID tags and a new handheld RFID detector shows promise for preoperative imaging-guided tumor localization.

An Adaptive Likelihood Distribution Algorithm for the Localization of Passive RFID Tags

The RFID (Radio Frequency IDentification) tag technology is expected as a tool of localization. By the localization of RFID tags, a mobile robot which installs in RFID readers can recognize surrounding environments. In addition, RFID tags can be applied to a navigation system for walkers. In this paper, we propose an adaptive likelihood distribution scheme for the localization of RFID tags. This method adjusts the likelihood distribution depending on the signal intensity from RFID tags. We carry out the performance evaluation of estimated position error by both computer simulations and implemental experiments. We show that the proposed system is more effective than the conventional system.

Localization and Tracking of Passive RFID Tags Based on Direction Estimation

Radio frequency identification (RFID) is poised for growth as businesses and governments explore applications implementing RFID. The RFID technology will continue to evolve to meet new demands for human and target location and tracking. In particular, there are increasing needs to locate and track multiple RFID-tagged items that are closely spaced. As a result, localization and tracking techniques with higher accuracy yet low implementation complexity are required. This paper examines the applicability of direction-of-arrival (DOA) estimation methods to the localization and tracking problems of passive RFID tags. Different scenarios of stationary and moving targets are considered. It is shown through performance analysis and simulation results that simple DOA estimation methods can be used to provide satisfactory localization performance.