Reference Tags Research Articles

An RFID Indoor Positioning Algorithm Based on Support Vector Regression.

Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities.

RILS: RFID indoor localization system using mobile readers

The radio frequency identification technology was given greater interest as it is widely used for identification and localization in the cognitive radio sensor networks. While radio frequency identification–based indoor localization is attractive, the need for a large-scale and high-density deployment of readers and reference tags is costly. Using mobile readers mounted on guide rails, we design and implement an RFID indoor localization system, which requires neither reference tags nor received signal strength indicator functions, for stock-taking and searching in warehouse operations. In particular, we install two guide rails, which can allow a reader to move horizontally or vertically, on the ceiling of a warehouse or workshop. We then propose a continuous scanning algorithm to improve the accuracy for locating a single tagged object and a category-based scheduling algorithm to shorten the time for locating multiple tagged objects. Our primary experimental results show that RFID indoor localization system can achieve high time efficiency and localization accuracy in the indoor localization.

TagFree Activity Identification with RFIDs

Human activity identification plays a critical role in many Internet-of-Things applications, which is typically achieved through attaching tracking devices, e.g., RFID tags, to human bodies. The attachment can be inconvenient and considered intrusive. A tag-free solution instead deploys stationary tags as references, and analyzes the backscattered signals that could be affected by human activities in close proximity. The information offered by today's RFID tags however are quite limited, and the typical raw data (RSSI and phase angles) are not necessarily good indicators of human activities (being either insensitive or unreliable as revealed by our realworld experiments). As such, existing tag-based activity identification solutions are far from being satisfactory, not to mention tag-free. It is also well known that the accuracy of the readings can be noticeably affected by multipath, which unfortunately is inevitable in an indoor environment and is complicated with multiple reference tags. In this paper, we however argue that multipath indeed brings rich information that can be explored to identify fine-grained human activities. Our experiments suggest that both the backscattered signal power and angle are correlated with human activities, impacting multiple paths with different levels. We present TagFree, the first RFID-based device-free activity identification system by analyzing the multipath signals. Different from conventional solutions that directly rely on the unreliable raw data, TagFree gathers massive angle information as spectrum frames from multiple tags, and preprocesses them to extract key features. It then analyzes their patterns through a deep learning framework. Our TagFree is readily deployable using off-the-shelf RFID devices and a prototype has been implemented using a commercial Impinj reader. Our extensive experiments demonstrate the superiority of our TagFree on activity identification in multipath-rich environments.

An accurate real-time RFID-based location system

Modern applications frequently require the ability to locate objects in real-world environments. This has motivated the development of a number of competing approaches to object localisation, most of which target specific applications. Radio Frequency IDentification (RFID) has emerged as a viable platform for localisation, but due to a number of unresolved challenges with this technology, high levels of performance and wide applicability have remained elusive. In this paper, we outline an RFID-based object localisation framework that addresses these challenges, and propose the use of Received Signal Strength (RSS) to model the behaviour of radio signals decaying over distance in an orientation-agnostic manner to simultaneously locate multiple stationary and mobile objects. The proposed localisation system can operate in a realistically radio-noisy indoor environment, enables design-space trade-offs, is highly extensible, and provides use-case-driven average accuracy as low as 0.15 metres. The proposed localisation system can quickly locate objects with or without the use of reference tags, and illustrates that RSS can be a reliable metric for RFID-based object localisation.

An accurate real-time RFID-based location system

Modern applications frequently require the ability to locate objects in real-world environments. This has motivated the development of a number of competing approaches to object localisation, most of which target specific applications. Radio Frequency IDentification (RFID) has emerged as a viable platform for localisation, but due to a number of unresolved challenges with this technology, high levels of performance and wide applicability have remained elusive. In this paper, we outline an RFID-based object localisation framework that addresses these challenges, and propose the use of Received Signal Strength (RSS) to model the behaviour of radio signals decaying over distance in an orientation-agnostic manner to simultaneously locate multiple stationary and mobile objects. The proposed localisation system can operate in a realistically radio-noisy indoor environment, enables design-space trade-offs, is highly extensible, and provides use-case-driven average accuracy as low as 0.15 metres. The proposed localisation system can quickly locate objects with or without the use of reference tags, and illustrates that RSS can be a reliable metric for RFID-based object localisation.

The RFID data clustering algorithm for improving indoor network positioning based on LANDMARC technology

Radio frequency identification (RFID) is a technique that uses radio frequency signals to automatically position the target. RFID-based location identification based on dynamic active RFID calibration (LANDMARC) system has prominent superiority in indoor positioning of complex environment. However, the original LANDMARC algorithm has disadvantages such as few reference tags, impractical weight definition, wrong selection for neighbor tags, unreasonable layout of readers and reference tags. In the work, an improved LANDMARC system was established by using 4 readers, 4 nearest neighbors and 28 reference tags. After that, the wrong neighbor tags were judged by distance constraint. The RSSI value was divided into 128 energy levels to set weight formula of index neighbor tag, thus obtaining more ideal indoor positioning effect.

An Accurate Indoor RSSI Localization Algorithm Based on Active RFID System with Reference Tags

Radio frequency identification technologies are popular since their cost is very low and its data transmission based upon radio-wave communications. Where, the objects that are attached to tags are located using the reference tags. However, RSSI information suffers from the multipath propagation and anisotropic interference. So, the localization accuracy will be affected severely. Also, the multipath-propagation increases whenever the reference-tags increase, and so does for the cost, and signal interference. This paper presents a boundary virtual reference label algorithm for improving the indoor-efficiency by inserting some virtual reference tags on the boundary with establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The Results show that the localisation precision of the proposed approach has significantly increased, at least 78% without adding extra reference tags or radio frequency interference which represents a significant improvement over other algorithms .

Indoor Localization Using the Reference Tags and Phase of Passive UHF-RFID Tags

In recent years, indoor position has been an important role in many applications, such as production management, store management and shelves in supermarket or library. Much time and energy are exhausted because one object cannot be quickly and accurately located. Traditional indoor position systems have some problems, such as complicated software and hardware system, inaccurate position and high time complexity. In this paper, the authors propose an RFID-based collaborative information system, Tagrom, for indoor localization using COTS RFID readers and tags. Unlike former methods, Tagrom works with reference tags and phase of Passive UHF-RFID tags, which improves traditional distribution of reference tags and utilize RF phase replace of traditional RSSI or multipath profile to determine the position of target RFID tags.

Application of the Acoustic Propagation Model to a deep‐water cross‐shelf curtain

Summary A good understanding of acoustic coverage and temporal variation relative to environmental conditions is crucial for accurate interpretation of results from acoustic tracking studies and ensuing appropriate management recommendations. In their recent paper, Gjelland & Hedger (Methods in Ecology and Evolution, 2017) suggest that the general detection probability model proposed by Gjelland & Hedger (Methods in Ecology and Evolution, 2013, 4, 665–674) was not appropriately used in Huveneers et al. (Methods in Ecology and Evolution, 2016, 7, 825–835). The intent of the comparison in Huveneers et al. (2016) was to evaluate the Acoustic Propagation Model (APM) in a situation when parameterisation is not logistically feasible. This can be the case when reference tags have not been deployed, or when the distance between tagged animals and receivers cannot be accurately estimated or cannot be obtained under sufficiently varied weather conditions. Even when parameterisation is possible, there will be situations where the APM does not account for all factors affecting detection probability, e.g. deep‐water receivers where density gradients can affect sound propagation. Re‐parameterisation of the APM based on clarification in Gjelland & Hedger (2017) and application to a deep‐water cross‐shelf receiver array showed similar detection range to a logistic model until about ˜750 m distance, after which the APM resulted in unrealistically high detection probability estimates. The need for large amounts of data to parameterise the APM and achieve good statistical fit negates the value of the theoretical propagation model. Detection range can also be affected by a broad range of factors, many of which are not included within the APM. The complexity of the way different environmental factors can influence acoustic detections and the variability of environments in which acoustic tracking studies are undertaken make it challenging to develop a general model applicable across environments. We support further improvement of the APM and recommend the use of reference tags to collect necessary data to parameterise the APM and assess factors influencing detection probability.

An Indoor Multi-Tag Cooperative Localization Algorithm Based on NMDS for RFID

For radio frequency identification (RFID) system, the location information is very important in many applications, such as Internet of Things, logistics, library management, and so on. Most of traditional RFID localization algorithms can only locate one tag at a time. However, multitag localization is desired in many RFID applications. In this paper, we propose a novel range-free algorithm named nonmetric multidimensional scaling (NMDS)-RFID(F), which combines NMDS algorithm and the fingerprinting localization algorithm to achieve indoor RFID multitag cooperative localization. The NMDS-RFID(F) algorithm firstly uses received signal strength Euclidean distance based on fingerprinting method to get the rank order of the distance for all pairs of tags, and then NMDS algorithm is used to generate the relative coordinates of tags. Finally, due to the coordinates of reference tags are known, we can use the coordinate system registration algorithm to get the absolute coordinates of tags. Simulation results show that the NMDS-RFID(F) algorithm outperforms some previous RFID localization algorithms.

PATL: A RFID Tag Localization based on Phased Array Antenna

In RFID systems, how to detect the position precisely is an important and challenging research topic. In this paper, we propose a range-free 2D tag localization method based on phased array antenna, called PATL. This method takes advantage of the adjustable radiation angle of the phased array antenna to scan the surveillance region in turns. By using the statistics of the tags’ number in different antenna beam directions, a weighting algorithm is used to calculate the position of the tag. This method can be applied to real-time location of multiple targets without usage of any reference tags or additional readers. Additionally, we present an optimized weighting method based on RSSI to increase the locating accuracy. We use a Commercial Off-the-Shelf (COTS) UHF RFID reader which is integrated with a phased array antenna to evaluate our method. The experiment results from an indoor office environment demonstrate the average distance error of PATL is about 21 cm and the optimized approach achieves an accuracy of 13 cm. This novel 2D localization scheme is a simple, yet promising, solution that is especially applicable to the smart shelf visualized management in storage or retail area.

Localization Performance Analysis for Passive UHF RFID Technology Employing Directional Antennas and Omnidirectional Antennas

By the advantages of small size, low cost and easy deployment, passive Ultra High Frequency Radio Frequency Identification (UHF RFID) technology has become one of the most popular indoor localization technologies. As the most common measurement information, Received Signal Strength Indicator (RSSI) is severely affected by the complex indoor environments, like multipath propagation, Non-Line-of-Sight propagation, noise, etc. It eventually reduces location estimation accuracy. To improve the accuracy of RSSI-based algorithms, pattern matching algorithms are utilized with extra reference tags at fixed and known locations. One of the typical pattern matching algorithms is k-Nearest Neighbor (k-NN) algorithm. There is a problem for pattern matching algorithms utilized into real applications. That is the usually utilized RFID antenna is directional in industries while the algorithms are tested with omnidirectional antennas in experimental tests during research. In this paper, we take k-NN as an example to further analyze the performance difference between these two kinds of antennas. Experimental results show that the localization performance of k-NN algorithms with directional antennas utilized is worse than the results with omnidirectional antennas. But the performance is no significant difference.

RFID Based Indoor Positioning System Using Event Filtering

Recently, location systems using RFID technology have been studied in indoor environments. However, the existing techniques require high computational cost to compute the location of a moving object because they compare the location proximity of all reference tags and objects. In this paper, we propose an RFID based location positioning scheme using event filtering, which reduces the computation cost of calculating the locations of moving objects while maintaining the accuracy of location estimation. In addition, we propose an incremental location update policy to reduce the location update cost for moving objects. We also compare the proposed scheme with one of the localization schemes, LANDMARC using a performance evaluation. As a result, the proposed scheme outperforms LANDMARC in terms of the computational cost of location estimation. The proposed scheme also reduces the cost of location update by using the RFID-based update policy.

Similarity Analysis-Based Indoor Localization Algorithm With Backscatter Information of Passive UHF RFID Tags

Passive radio frequency identification (RFID) is a low cost and low complexity localization technology. With introduction of reference tags, RFID technology can provide on-line reference information for pattern matching localization algorithms. One of the most popular algorithms is $k$ -nearest neighbor algorithm. While how to decide the $K$ value in estimation is still a problem. In addition, traditional received signal strength indicator (RSSI)-based algorithms suffer from multipath effects, it is difficult to further improve the localization accuracy. In this paper, we analyze the similarity of backscatter signals and present a novel range based indoor localization method, SAIL. According to the propagation distances of signals, reference tags that have similar distances to a reader antenna can be considered as a group. Inspired by the idea of grouping, clustering algorithm is utilized to get candidate tags for ranging. This method does not need to fix the number of reference tags in estimation. Besides RSSI, the RFID reader can also extract phase of signals coming from backscattered signals of responding tags. This guides us to combine RSSI and phase in similarity measure for clustering. In the simulation and experimental tests, SAIL is superior to other RFID schemes considering cost, location estimation error, and flexibility.

Setup-Independent Phase-Based Sensing by UHF RFID

By processing the phase of the backscattered field from a radio frequency identification ultrahigh-frequency tag antenna, physical information can be derived about the change of the materials and boundary conditions around a sensor-less tag. The phase-based sensing, however, is strongly dependent on the specific setup arrangement, and huge artifacts would arise in successive independent measurements involving temporary (handheld) readers at different distances. This letter introduces a multiport tag arrangement suitable to achieve distance-independent measurements. The method exploits the use of sensitive and reference tags. Their phase responses are combined within a double phase difference metric suitable for robust data inversion. The method is demonstrated through an experiment with a displacement-sensing system.

A Compact Dual-Polarized Chipless RFID Tag by Using Nested Concentric Square Loops

This letter presents a compact dual-polarized chipless tag. The chipless tag consists of nested concentric coplanar square loops, and adjacent loops are connected by two stub lines at the center of the opposite sides, respectively. There are two critical factors in achieving small size. One is due to the integration of multiple square loops in a nested structure, sharing the same sides. The structure is designed to obtain the following function relationship between the loop number $n$ and resonant mode number $N: N\,= \,2(n- 1)$ . The other critical factor for minimization is that it is the coupling of adjacent loops that responds to resonances. The gap size only influences the coupling strength, and shifts resonance frequency a little, and does not influence the sharp-edged resonance in spite of small gap. This is one highlight. The other highlight is that four variations can be independently adjusted on the basis of the proposed reference tag, and a lot of different encoding identifications can be obtained. In addition, the proposed tag also has the following advantages. The proposed tag is magnetic, and the radar cross section (RCS) magnitude is insensitive to the medium where it is pasted. High RCS magnitude is realized at the condition of minimization. The connected coplanar square loop resonators are reused in both vertical and horizontal polarizations to double the encoding capacity within a fixed bandwidth. The RCS curve keeps unchanged until the orientation angle rotates up to 35°. An 18-bit tag is developed by the proposed nested loops, and ${\text{19bits/cm}}^{2}$ data density is obtained.

Accurate RFID localization algorithm with particle swarm optimization based on reference tags

Accurate RFID localization algorithm with particle swarm optimization based on reference tags

TrackT: Accurate tracking of RFID tags with mm-level accuracy using first-order taylor series approximation

Radio Frequency Identification (RFID) technology is widely used to achieve indoor object tracking and positioning. Currently, many methods need to deploy a large number of reference tags beforehand and some are limited by antennas’ spacing. Further, the signal propagation along Non-Line of Sight introduces multipath effects which will challenge the accuracy of RFID localization system. In this work, we propose a method based on measured phase to track mobile RFID tags with millimeter level (mm-level) accuracy. We first partition the surveillance region into square grids at mm-level and suppose that there is a virtual tag as the same as the tracked one in each grid. On this basis, for the case where the tags move along a known track with constant speed, we only need to locate the tag's initial position. We leverage phase periodicity to obtain some candidates and then eliminate position ambiguity by double difference true phase. And for the case where the tag's moving track is unknown to the system, we adopt a first-order Taylor series expansion to calculate the relative displacements of the tracked tag and then locate the initial position as the same process as tracking the known trajectory. In our experiment, our solution can achieve a mean error distance of 0.26cm and 0.55cm for known and unknown movement tracks respectively.

Real-Time Locating Systems Using Active RFID for Internet of Things

The proliferation of the Internet of Things (IoT) has fostered growing attention to real-time locating systems (RTLSs) using radio frequency identification (RFID) for asset management, which can automatically identify and track physical objects within indoor or confined environments. Various RFID indoor locating systems have been proposed. However, most of them are inappropriate for large-scale IoT applications owing to severe radio multipath, diffraction, and reflection. In this paper, we propose a newly fashioned RTLS using active RFID for the IoT, i.e., iLocate, which locates objects at high levels of accuracy up to 30 cm with ultralong distance transmission. To achieve fine-grained localization accuracy, iLocate presents the concept of virtual reference tags. To overcome signal multipath, iLocate employs a frequency-hopping technique to schedule RFID communication. To support large-scale RFID networks, iLocate leverages the ZigBee. We implement all hardware using 2.45-GHz RFID chips so that each active tag can communicate with readers that are around 1000 m away in a free space. Our empirical study and real project deployment show the superiority of the proposed system with respect to the localization accuracy and the data transmission rate for large-scale active RFID networks.