Passive Indoor Leveled RFID Localization Algorithms PDF Download

Author: Matthew Chan
Publisher:
ISBN: 9781303087776
Category :
Languages : en
Pages : 172

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Book Description
One of the most sought-after innovations in RFID technology is the ability to accurately locate stationary objects and track moving entities in real time. The author proposes three multi-leveled detectable count RFID localization algorithms (nearest-neighbor, multilateration, Bayesian inference) to accomplish these tasks using UHF passive RFID tags---chosen due to low cost and efficient implementation---by affixing them onto the floor as known reference nodes. Simulations are conducted to examine the accuracy and performance of the algorithms to locate stationary and mobile objects. Furthermore, experiments are carried out to test the localization of stationary objects in a real world setting such as a laboratory environment. The outcomes from the simulations and experiments are analyzed. The results are remarkable and most importantly, when the proper parametric values are considered, such as reference tag density and detection range, the accuracy performance of the algorithms achieved are impressive which confirms that the proposed methods are highly preferable when accurate, efficient and cost-effective passive RFID localization systems are to be implemented. Future directions of the study include exploration of different ratios for the three power levels of the RFID reader, use of other reference tag spacing pattern besides square such as hexagon, examination of other multi-level approach beside tri-level such as quad-, penta- or dual-level, experimentation with different kinds of RFID reference tags besides the passive Alien type G, as well as field tests of methods for mobile entities in a realistic real-world settings such as a laboratory.

Author: Alseny Diallo
Publisher:
ISBN:
Category : Global Positioning System
Languages : en
Pages :

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Book Description
Location-based services (LBSs) are significantly becoming a vital part of life. Global Navigation Satellite Systems such as GPS have been used in a wide range of applications, from transport navigation, and synchronization of telecommunications networks. GPS works remarkably well in outdoor positioning. However, GPS does not perform well when the target object is close to walls, buildings, trees, or indoor environments. This is because the signal from the GPS satellites is too weak to penetrate an obstacle like concrete walls and buildings. Due to the complicated characteristics of indoor environments, the development of an indoor localization technique is always linked with a set of difficulties. For instance, indoor localization systems are challenged by the influence of obstacles like equipment, movement of human beings, doors, walls, and more. To overcome those challenges, we propose an indoor localization system that uses radio frequency identification tags or RFID tags to localize moving targets. Our indoor localization algorithm derives the accurate location of a target object using a set of RFID tags deployed in a predefined area. Our localization algorithm derives the target device’s location based on the signal it receives from surrounding tags. Depending on the number of tags in the surrounding area, the algorithm uses different formulas to calculate the target’s location. Specifically, there are three different cases. A case where a target object is nearby one tag, two tags or three tags. The localization algorithm works well when an area is well covered by tags. To optimize the coverage using a limited number of tags, we developed a greedy algorithm that uses a novel score function to help deploy the RFID tags in a predefined area to guarantee maximum coverage. This approach is evaluated using both simulation and real system use cases. Additionally, we designed a system deployment that uses the localization algorithm and the tag deployment algorithm to track several target objects in a 2-dimensional space. The system is proven to be reliable, fault-tolerant and scale very well with an increase in workload. The results show our approach is effective in localizing target objects with high accuracy.

Author: Ramprabhu Jayaraman
Publisher:
ISBN:
Category : Radio frequency identification systems
Languages : en
Pages : 70

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Book Description
Passive radio frequency identification (RFID) systems are revolutionizing the indoor positioning and tracking applications. There has been substantial research on practical applications of this technology and hospitals especially trauma care units are one such area where this capability can lead to improved workflow. Our system uses the Alien RFID reader and the "Squiggle" passive RFID tags to create an effective solution for tracking various medical items. Based on the Received Signal Strength Indication (RSSI) value of the tags, we developed a localization algorithm which uses a neural network estimator to estimate the distances of the tags. To reduce the effect of noise in the RSSI values received from the reader, we accumulate data over a period of time, remove the outliers and the average the remaining RSSI values. The RSSI based estimation algorithm provides very accurate estimation when the spatial density of tags is low (about 25 tags per square meter). To improve the localization accuracy at higher spatial densities we augmented the RSSI method of estimating distances by using the number of times the tags were read or the "read-count'. We also investigated how different types of occluding materials affect the localization accuracy. Metal and Humans also can cause complete occlusion when the positioned in direct line of sight between the antenna and the tag. To overcome human based occlusion, we placed an additional ceiling mounted antenna per 10 m2. This intervention makes possible the detection (but not localization) of tags when the vertical field of view is not occluded. We also studied the effect of the material to which the tags are attached and determined the effects on localization accuracy. The software system developed using Java is designed in a modular fashion and provides interfaces to tools like Matlab so that it is easy to experiment to various other localization algorithms. We also developed an intuitive User Interface to display the locations of tags and the associated items. Once a tag is identified its associated description can be looked up in a computer database and this also can be displayed in the user interface.

Author: Jiaqing Wu (Engineer)
Publisher:
ISBN: 9781267797865
Category : Radio frequency identification systems
Languages : en
Pages : 183

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Book Description
Radio Frequency Identification (RFID) is an information exchange technology based on radio waves communication. It is also a possible solution to indoor localization. Due to multipath propagation and anisotropic interference in the indoor environment, theoretical propagation models are generally not sufficient for RFID-based localization. In fact, the radio frequency (RF) signal distribution may not even be monotonic and this makes range-based localization algorithms less accurate. On the other hand, range free localization algorithms, such as k Nearest-Neighbor (kNN), require reference tags to be spread throughout the whole three-dimensional (3D) space which is simply not practical. In this work, a hybrid real-time localization algorithm that combines reference tags with Received Signal Strength Indicator (RSSI) ranging is introduced to improve RFID-based 3D localization in high-complexity indoor environments. The experiments demonstrate that the proposed system is more accurate than traditional algorithms under real world constraints. The active RFID system includes 4 readers and 24 reference tags deployed in a fully furnished room. The localization algorithm is implemented in MATLAB and is synchronized with RF signal data collection in real-time. The results show that the novel hybrid algorithm achieves an average 3D localization error of 1.08m which represents a significant improvement over kNN and RSSI algorithms under the same circumstance. A battery-assisted passive RFID system was deployed side-by-side to the active system for comparison. Furthermore, the reader and tag performance was evaluated in both high-complexity laboratory environment and International Space Station (ISS) mock-up with high-reflection interior surface. In addition, theoretical models on minimum number of required reference tags and localization error prediction were introduced.

Author: Jing Wang
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Indoor localization with proximity information in ultra-high-frequency (UHF) radio-frequency-identification (RFID) is widely considered as a potential candidate of locating items in Internet-of-Things (IoT) paradigm. First, the proximity-based methods are less affected by multi-path distortion and dynamic changes of the indoor environment compared to the traditional range-based localization methods. The objective of this dissertation is to use tag-to-tag backscattering communication link in augmented UHF RFID system (AURIS) for proximity-based indoor localization solution. Tag-to-tag backscattering communication in AURIS has an obvious advantage over the conventional reader-to-tag link for proximity-based indoor localization by keeping both landmark and mobile tags simple and inexpensive. This work is the very first thesis evaluating proximity-based localization solution using tag-to-tag backscattering communication.Our research makes the contributions in terms of phase cancellation effect, the improved mathematical models and localization algorithm. First, we investigate the phase cancellation effect in the tag-to-tag backscattering communication, which has a significant effect on proximity-based localization. We then present a solution to counter such destructive effect by exploiting the spatial diversity of dual antennas. Second, a novel and realistic detection probability model of ST-to-tag detection is proposed. In AURIS, a large set of passive tags are placed at known locations as landmarks, and STs are attached mobile targets of interest. We identify two technical roadblocks of AURIS and existing localization algorithms as false synchronous detection assumption and state evolution model constraints. With the new and more realistic detection probability model we explore the use of particle filtering methodology for localizing ST, which overcomes the aforementioned roadblocks. Last, we propose a landmark-based sequential localization and mapping framework (SQLAM) for AURIS to locate STs and passive tags with unknown locations, which leverages a set of passive landmark tags to localize ST, and sequentially constructs a geographical map of passive tags with unknown locations while ST is moving in the environment. Mapping passive tags with unknown locations accurately leads to practical advantages. First, the localization capability of AURIS is not confined to the objects carrying STs. Second, the problem of failed landmark tags is addressed by including passive tags with resolved locations into landmark set. Each of the contributions is supported by extensive computer simulation to demonstrate the performance of enhancements.

Author: Jinsong Han
Publisher: Springer
ISBN: 3319126466
Category : Computers
Languages : en
Pages : 66

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Book Description
This SpringerBrief examines the use of cheap commercial passive RFID tags to achieve accurate device-free object-tracking. It presents a sensitive detector, named Twins, which uses a pair of adjacent passive tags to detect uncooperative targets (such as intruders). Twins leverages a newly observed phenomenon called critical state that is caused by interference among passive tags. The author expands on the previous object tracking methods, which are mostly device-based, and reveals a new interference model and their extensive experiments for validation. A prototype implementation of the Twins-based intrusion detection scheme with commercial off-the-shelf reader and tags is also covered in this SpringerBrief. Device-Free Object Tracking Using Passive Tags is designed for researchers and professionals interested in smart sensing, localization, RFID and Internet of Things applications. The content is also useful for advanced-level students studying electrical engineering and computer science.

Author: Zubin Shah
Publisher:
ISBN:
Category : Radio frequency identification systems
Languages : en
Pages : 69

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Book Description
Radio frequency identification (RFID) based localization systems provide a unique approach to localize mobile entities equipped with RFID readers or tagged with RFID tags. UHF RFID systems using passive tags are a good choice considering their cost, reading range, and reliability. With global acceptance and deployment of UHF RFID systems using passive tags for tracking and identification, virtually everything around us can be tagged with small and low-cost passive RFID tags. This thesis describes a Monte Carlo Localization based algorithm to localize a mobile RFID reader within a tagged environment. A software tool is developed to validate this localization process, simulate it and analyze its performance. Requirement specification for such RFID based localization systems can be determined based on various analysis plots available from this software tool. The tool also analyses tag localization using fixed readers to study the RFID characteristics for localization. These localization approaches can be used to provide intelligent context aware services.

Author: Bhavik Contractor
Publisher:
ISBN:
Category : Radio frequency identification systems
Languages : en
Pages : 75

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Book Description
The advent of GPS has redefined the need of a positioning system in today*’s ubiquitous computing world. While GPS works satisfactorily and is quite a norm in an outdoor environment, it fails to work indoors due to the inherent complexity of an indoor environment. There is an ever increasing need to develop an indoor positioning system and a lot of research has been done to solve the problem of indoor localization. These solutions differ on the basis of cost, dependency on environment, line of sight requirements and so on. Passive RFID (Radio Frequency Identification) tags pose an interesting solution to the problem of indoor localization, given the ease of deployment and the cost effective infrastructure. It is less expensive to tag items with RFID tags, than to attach them with sensor nodes. In this thesis, the problem of using the RFID technology for two dimensional indoor localization is studied. A relatively inexpensive technique requiring just one RFID reader and multiple passive RFID tags is adopted. The idea is to use multilateration among the passive tags to solve the problem of localization. A ranging technique is developed to establish a relationship between signal strength and distance. The concept of Received Signal Strength calibration is used to develop the ranging technique, and to account for the effects of dynamic environmental conditions on localization. Finally, an error map matching technique is employed to counter the errors in localization.

Author: Akshay Shetty
Publisher:
ISBN:
Category : Medical care
Languages : en
Pages : 62

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Book Description
Technologies using identification by radio frequencies (RFID) are experiencing rapid development and healthcare is a major application area benefiting from it. Highly pervasive RFID enables remote identification, tracking and localization of the medical staff, patients, medications and equipment, thus increasing safety, optimizing in real-time management and providing support for new ambient-intelligent services. This thesis describes and evaluates an algorithm that enables object localization and tracking using passive RFID tags. This thesis also describes scenarios of how this technology can be used as a part of building a smart trauma resuscitation room by tracking the equipments. The main contribution of this thesis is the adaptation of the Weighted K-Nearest Neighbor Algorithm as a localization technique to track objects in a confined and crowded space by using passive RFID tags. The input parameter to the algorithm is the received signal strength indicator (RSSI), which gives a measure of back-scattered radio frequencies from passive tags. While using RFID technology special attention has to be given to the placement of antennas to get the optimum result. Therefore, we analyzed various antenna placement configurations with mean error and error consistency as the two performance parameters. The detection of multiple tags and human occlusion are two major concerns while tracking tags in a confined space with many team members collaborating on solving a problem. The RF signal can be interrupted by people walking around randomly and holding multiple (tagged) instruments at the same time. While the algorithm worked fine when tracking multiple tags, we had to modify the experimental set-up and attach an antenna onto the ceiling (which we call a vertical antenna), so that even if all the wall antennas are blocked we get at least one input parameter to base our localization decision on. We evaluated the algorithm for different combinations of configurations and number of neighbors, and achieved the following results. The best results were obtained for the 3 antennae (placed orthogonally) configuration considering the 4 nearest neighbors wherein a mean error rate of 15% of the maximum possible error was achieved under ideal conditions. We tested the algorithm for different human occlusion scenarios i.e. blocking 1 or 2 wall antennas, standing in random positions and then roaming in the field area randomly. The mean error rate for the standing scenario was measured as 20% of the maximum possible error and 18% in the case of roaming configuration. The error was found to be consistently within our defined maximum error for 100% of the recorded readings. The results obtained were found to be satisfactory for our application where, more than the exact location of the object, knowing whether the object is within a particular region is good enough for the users to know what task is being carried out in the trauma bay. Also the algorithm holds good in an indoor environment having a lot of factors and materials which affect the RF signal disrupting accurate calculation of the location co-ordinates. The algorithm does not require extensive data collection prior to implementation which makes it easily deployable in any environment. Apart from the problems mentioned there are some other factors like materials on which the tags are attached and orientation of tags which were found to be potential hindrances for accurate localization. Acceptable solutions to these problems form a part of our future work.

Author: Nanda Gopal Jeevarathnam
Publisher:
ISBN:
Category : Radio frequency identification systems
Languages : en
Pages : 60

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Book Description
Passive ultra-high frequency (UHF) radio frequency identification (RFID) systems have gained immense popularity in recent years for their wide-scale industrial applications in inventory tracking and management. In this study, we explore the potential of passive RFID systems for indoor localization by developing a grid-based experimental framework using two standard and easily measurable performance metrics: received signal strength indicator (RSSI) and tag read count (TRC). We create scenarios imitating real life challenges such as placing metal objects and other RFID tags in two different read fields (symmetric and asymmetric) to analyze their impacts on location accuracy. We study the prediction potential of RSSI and TRC both independently and collaboratively. In the end, we demonstrate that both signal metrics can be used for localization with sufficient accuracy whereas the best performance is obtained when both metrics are used together for prediction on an artificial neural network especially for more challenging scenarios. Experimental results show an average error of as low as 0.286 (where consecutive grid distance is defined as unity) which satisfies the grid-based localization benchmark of less than 0.5.