scholarly journals IKULDAS: An Improved kNN-Based UHF RFID Indoor Localization Algorithm for Directional Radiation Scenario

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 968 ◽  
Author(s):  
Weiguang Shi ◽  
Jiangxia Du ◽  
Xiaowei Cao ◽  
Yang Yu ◽  
Yu Cao ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Rotation Angle ◽  
Localization Algorithm ◽  
Uhf Rfid ◽  
Estimation Model ◽  
K Value ◽  
Directional Radiation ◽  
Reader Antenna

Ultra high frequency radio frequency identification (UHF RFID)-based indoor localization technology has been a competitive candidate for context-awareness services. Previous works mainly utilize a simplified Friis transmission equation for simulating/rectifying received signal strength indicator (RSSI) values, in which the directional radiation of tag antenna and reader antenna was not fully considered, leading to unfavorable performance degradation. Moreover, a k-nearest neighbor (kNN) algorithm is widely used in existing systems, whereas the selection of an appropriate k value remains a critical issue. To solve such problems, this paper presents an improved kNN-based indoor localization algorithm for a directional radiation scenario, IKULDAS. Based on the gain features of dipole antenna and patch antenna, a novel RSSI estimation model is first established. By introducing the inclination angle and rotation angle to characterize the antenna postures, the gains of tag antenna and reader antenna referring to direct path and reflection paths are re-expressed. Then, three strategies are proposed and embedded into typical kNN for improving the localization performance. In IKULDAS, the optimal single fixed rotation angle is introduced for filtering a superior measurement and an NJW-based algorithm is advised for extracting nearest-neighbor reference tags. Furthermore, a dynamic mapping mechanism is proposed to accelerate the tracking process. Simulation results show that IKULDAS achieves a higher positioning accuracy and lower time consumption compared to other typical algorithms.

Deep learning approach for UHF RFID-based indoor localization

2021 ◽  
pp. 1-13
Author(s):  
Haishu Ma ◽  
Zongzheng Ma ◽  
Lixia Li ◽  
Ya Gao
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Estimation Error ◽  
Research Direction ◽  
Linear Interpolation ◽  
Linear Mapping ◽  
Uhf Rfid ◽  
Indoor Location ◽  
Main Research

Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

A compact wideband circularly polarized quadrifilar antenna with PIFA elements for UHF RFID readers

2020 ◽  
Vol 12 (10) ◽  
pp. 1020-1028
Author(s):  
Chawanat Lerkbangplad ◽  
Alongkorn Namahoot ◽  
Prayoot Akkaraekthalin ◽  
Suramate Chalermwisutkul
Keyword(s):  
Frequency Band ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Impedance Matching ◽  
Axial Ratio ◽  
Uhf Rfid ◽  
Total Size ◽  
Circularly Polarized ◽  
Directional Radiation ◽  
Feed Network

AbstractIn this paper, a compact circularly polarized quadrifilar antenna with planar inverted-F antenna (PIFA) elements is presented. The proposed antenna consists of four PIFA elements and a Wilkinson divider-based feed network fabricated on FR-4 substrate (ɛr = 4.4, loss tangent = 0.02, thickness = 1.6 mm). The total size of the antenna is 120 × 120 × 13.2 mm3. Impedance matching with a reflection coefficient <−15 dB and an axial ratio (AR) <3 dB are achieved over the global ultra-high frequency (UHF) radio frequency identification (RFID) frequency band and beyond. The realized gain ranges from 2.25 to 3.75 dBic within the frequency band of interest from 860 to 960 MHz with a directional radiation pattern. The proposed antenna is compact, low-cost and extremely wideband in terms of matching and AR compared to state-of-the-art UHF RFID reader antennas.

scholarly journals An UHF RFID Reader Antenna with Multitag Identification for Extremely Low-Temperature Medical Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ji S. Jung ◽  
Jung N. Lee ◽  
Joung M. Kim ◽  
Jong K. Park
Keyword(s):  
Radio Frequency Identification ◽  
Symmetric Design ◽  
Uhf Rfid ◽  
Medical Systems ◽  
Power Dividers ◽  
High Isolation ◽  
The North ◽  
Reader Antenna ◽  
Frequency Identification ◽  
Two Hybrid

A radio frequency identification reader antenna having multitag identification for medical systems is presented, which consists of four PIFAs, two hybrid couplers, and four power dividers. The high isolation is achieved by the symmetric design of the antenna geometry and four power dividers, which are fed by two hybrid couplers. The experimental results show an isolation of more than 40 dB in the North American (902–928 MHz), Korean (917–923.5 MHz), and Japanese (916.7–923.5 MHz) RFID frequency bands.

scholarly journals Localization of passive UHF RFID Labels with Kalman Filter

2012 ◽  
Vol 10 ◽  
pp. 119-125 ◽  
Author(s):  
T. Nick ◽  
J. Götze
Keyword(s):  
Kalman Filter ◽  
Radio Frequency Identification ◽  
Unscented Kalman Filter ◽  
Uhf Rfid ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Passive Uhf Rfid ◽  
Reader Antenna ◽  
Reference Tags ◽  
Frequency Identification

Abstract. Localization via Radio Frequency Identification (RFID) is frequently used in different applications nowadays. It has the advantage that next to its ostensible purpose of identifying objects via their unique IDs it can simultaneously be used for the localization of these objects. In this work it is shown how Received Signal Strength Indicator (RSSI) measurements at different antennae of a passive UHF RFID label can be combined for localization. The localization is only done based on the RSSI measurements and a Kalman Filter (KF). Because of non-linearities in the measurement function it is necessary to incorporate an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) where simulations have shown that the UKF performs better than the EKF. Additionally to the selection of the filter there are different possibilities to increase the localization accuracy of the UKF: The advantages of using Reference Tags (RT) or more than one tag per trolley (relative positioning) in combination with an Unscented Kalman Filter are discussed and simulations results show that the localization error can be decreased significantly via these methods. Another possibility to increase the localization accuracy and in addition to achieve a more realistic simulation is the consideration of the angle between reader antenna and tag. Simulation results with the incorporation of different numbers of fixed antennae lead to the conclusion that this is a useful surplus in the localization.

Research on the Indoor Location Algorithm Based on RFID

2013 ◽  
Vol 421 ◽  
pp. 518-522
Author(s):  
Yi Zhang ◽  
Qi Shan Hou ◽  
Yuan Luo
Keyword(s):  
Nearest Neighbor ◽  
Computational Cost ◽  
Wireless Sensor ◽  
The Novel ◽  
Localization Algorithm ◽  
Target Node ◽  
K Value ◽  
Location Accuracy ◽  
Indoor Location ◽  
Location Algorithm

An improved localization algorithm was proposed to solve the problem of low location accuracy and large computational cost in nearest neighbor (KNN) algorithm for LANDMARC system. The novel algorithm combined RFID and wireless sensor networks. It divided location area into several sub areas, utilized the sensor network to locate the target node to corresponding sub area, removed the reference nodes far from the target node, narrowed down the selection scope of the k value, used KNN algorithm to calculate the coordinate of target node in the sub area and applied the Taylor series iteration to improve the accuracy. Experiment results shows that the proposed algorithm improves the location accuracy evidently.

scholarly journals WallSense: Device-Free Indoor Localization Using Wall-Mounted UHF RFID Tags

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 68 ◽  
Author(s):  
Liang Ma ◽  
Meng Liu ◽  
Hongjun Wang ◽  
Yang Yang ◽  
Na Wang ◽  
...  
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Fitness Function ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Swarm Optimization ◽  
Median Error ◽  
Frequency Identification ◽  
Device Free ◽  
Extract Information

To achieve device-free indoor localization without the active participation of the users, this paper presents WallSense, a device-free indoor localization system based on off-the-shelf Radio RFID (Radio-Frequency Identification) equipment. The system deploys two orthogonal tag arrays in adjoining walls and uses the RSSI and phase information measured by RFID readers to localize the target. By differentiating the backscattered signal between adjacent tag pairs, WallSense is able to eliminate most undesirable factors and extract information directly related to the location of the target. By applying Particle Swarm Optimization (PSO) with a novel weighted fitness function and combining the localization result of two orthogonal tag arrays, the system is able to localize the target with high accuracy. Experiments show that the system is able to localize human target with 0.24 m median error. Also, WallSense has low deployment overhead and do not require the users to carry any devices.

scholarly journals Uniform Magnetic Field Characteristics Based UHF RFID Tag for Internet of Things Applications

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1603
Author(s):  
Abubakar Sharif ◽  
Yi Yan ◽  
Jun Ouyang ◽  
Hassan Tariq Chattha ◽  
Kamran Arshad ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Radio Frequency Identification ◽  
Uniform Magnetic Field ◽  
Near Field ◽  
Field Performance ◽  
Logarithmic Spiral ◽  
Uhf Rfid ◽  
Rfid Tag ◽  
Sensing Applications ◽  
Reader Antenna

This paper presents a novel inkjet-printed near-field ultra-high-frequency (UHF) radio frequency identification (RFID) tag/sensor design with uniform magnetic field characteristics. The proposed tag is designed using the theory of characteristics mode (TCM). Moreover, the uniformity of current and magnetic field performance is achieved by further optimizing the design using particle swarm optimization (PSO). Compared to traditional electrically small near-field tags, this tag uses the logarithmic spiral as the radiating structure. The benefit of the logarithmic spiral structure lies in its magnetic field receiving area that can be extended to reach a higher reading distance. The combination of TCM and PSO is used to get the uniform magnetic field and desired resonant frequency. Moreover, the PSO was exploited to get a uniform magnetic field in the horizontal plane of the normal phase of the UHF RFID near-field reader antenna. As compared with the frequently-used commercial near field tag (Impinj J41), our design can be readable up to a three times greater read distance. Furthermore, the proposed near-field tag design shows great potential for commercial item-level tagging of expensive jewelry products and sensing applications, such as temperature monitoring of the human body.

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

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3286
Author(s):  
Yunlei Zhang ◽  
Xiaolin Gong ◽  
Kaihua Liu ◽  
Shuai Zhang
Keyword(s):  
Phase Difference ◽  
Radio Frequency Identification ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Localization Algorithm ◽  
Uhf Rfid ◽  
Dual Frequency ◽  
Tracking Accuracy ◽  
Vehicle Localization ◽  
Localization And Tracking

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.

scholarly journals Indoor Localization Method Based on Regional Division with IFCM

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 559 ◽  
Author(s):  
Junhuai Li ◽  
Xixi Gao ◽  
Zhiyong Hu ◽  
Huaijun Wang ◽  
Ting Cao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Pearson Correlation ◽  
Computation Time ◽  
Access Point ◽  
Target Point ◽  
Localization Algorithm ◽  
K Nearest Neighbor ◽  
Localization Method ◽  
Regional Division

With the development of wireless technology, indoor localization has gained wide attention. The fingerprint localization method is proposed in this paper, which is divided into two phases: offline training and online positioning. In offline training phase, the Improved Fuzzy C-means (IFCM) algorithm is proposed for regional division. The Between-Within Proportion (BWP) index is selected to divide fingerprint database, which can ensure the result of regional division consistent with the building plane structure. Moreover, the Agglomerative Nesting (AGNES) algorithm is applied to accomplish Access Point (AP) optimization. In the online positioning phase, sub-region selection is performed by nearest neighbor algorithm, then the Weighted K-nearest Neighbor (WKNN) algorithm based on Pearson Correlation Coefficient (PCC) is utilized to locate the target point. After the evaluation on the effect of regional division and AP optimization of location precision and time, the experiments show that the average positioning error is 2.53 m and the average computation time of the localization algorithm based on PCC reduced by 94.13%.

Experimental Investigation and Numerical Optimization of Key Factors Affecting the Programming Efficacy of Passive UHF RFID Tags

2013 ◽  
Author(s):  
Kassy M. Lum ◽  
Donnie Proffitt ◽  
Ann Whitney ◽  
Johné M. Parker
Keyword(s):  
Experimental Investigation ◽  
Radio Frequency Identification ◽  
System Performance ◽  
Laser Printer ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Key Factors ◽  
Rfid Systems ◽  
Rfid System ◽  
Reader Antenna

Radio Frequency Identification (RFID) is a disruptive technology that uses radio waves to uniquely identify objects. As such, it has the potential to bring significant benefits to numerous government and private sector initiatives. However, significant technical challenges remain. A key area of study is in system performance: while the major hardware components in an RFID system (i.e., tags, readers and middleware) have been and continue to be studied extensively, there has been little research, comparatively, in characterizing RFID system performance. The research presented in this paper was inspired, in part, by a laser printer RFID solution; i.e., one in which the printer simultaneously prints and programs ultra-high frequency (UHF) tags embedded in print media. In this paper, we have conducted a detailed experimental investigation of the primary factors influencing the performance of RFID systems similar to the print solution. This study aims to provide a systematic experimental process for investigating key factors — e.g., the air gap between reader antenna and tag, in-plane orientation of the tag with respect to the reader antenna, and power level output of the reader — which affect the programmability of UHF RFID tags. Results provide a baseline evaluation of the functionality of RFID systems of similar designs and provide a basis for a detailed exploration of the primary factors which affect RFID UHF passive tag dynamic programming capabilities. By understanding which factors significantly affect the readability and programming of RFID tags, this research suggests optimal designs for system functionality and provides data needed in order to advance such designs. Additionally, a key obstacle for RFID implementation is tag selection. Effectively matching tags to applications requires numerous economic and technical considerations; these considerations generate different implementation constraints. This paper lays the foundation for a multi-objective optimization algorithm to help determine optimal tag selection for a given application, based upon tag performance and cost.

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