Indoor distance estimation for passive UHF RFID tag based on RSSI and RCS

Omer, Mohamed I.; Tian, Gui Yun

doi:10.1016/j.measurement.2018.05.116

Cited by 52 publications

(22 citation statements)

References 23 publications

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“…Thus, a large proportion of the relevant research is focused on the development of positioning measurement systems that overcome the drawbacks of the GPS-based ones [37][38][39][40][41][42][43].…”

Section: Localization Of Mobile Iot Nodesmentioning

confidence: 99%

Research challenges in Measurement for Internet of Things systems

et al. 2019

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In this paper, an overview of the research challenges in measurements for the design of Internet of Things (IoT) systems is proposed. To this end, a general architecture of an IoT system is presented, which is specialized according to two key requirements: the power supply capabilities of the infrastructure and the time delay constraints of the application. Guidelines for the design of an IoT system are summarized, and the measurement needs are highlighted. A review of the research contributions is given concerning three main measurement topics: (i) energy-aware data acquisition systems, (ii) localization of mobile IoT nodes, and (iii) precise synchronization protocols.

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Section: Localization Of Mobile Iot Nodesmentioning

confidence: 99%

Research challenges in Measurement for Internet of Things systems

et al. 2019

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“…Depending on the received power the distance could be estimated by using a signal propagation model [1,13,22] as shown in Equation 1. Most methods and algorithms for monitoring and locating objects are based on signal propagation.…”

Section: Tag Factors Characterizationmentioning

confidence: 99%

“…In fact, there are several developed methods used for distance estimation combining with fingerprint technique [22,23]. These methods may include probabilistic methods, e.g., Bayesian learning method, Kalman filtering [16], or they may include machine learning localization algorithms as a support vector machine (SVM) [12].…”

Section: Classification Algorithm Based On Rssi Fingerprint Databasementioning

confidence: 99%

Indoor Localization Systems for Passive Uhf Rfid Tag Based on Rssi Radio Map Database

Omer¹,

Ran²,

Tian³

2019

PIER M

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A classification algorithm is applied to UHF radio frequency identification (RFID) system to estimate the indoor position of a passive tag utilizing a received signal strength indicator (RSSI). Passive tag signals are collected by conventional UHF RFID reader antenna located at two different positions. K-NN and curve fitting are used for distance estimation and examined at different frequencies within the range 902-928 MHz to find suitable frequencies which can minimize the average error. In the proposed method, the measured RSSI values are compared with the fingerprint database in different frequencies to find the nearest neighbor. The best accuracy achieved at frequency range 926-928 MHz is 18.3 cm. The monitoring system is composed of a reader and tags under test, which makes the proposed system robust, easy to set up, and with low cost. The limitation of the proposed method is also discussed.

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“…In general, to determine the location of an object based on the received signal strength, you mainly need two pieces of information: the transmitter s location, and the signal strength at the transmitter. In [22], the researchers investigated a relationship between the radar cross section (RCS) and the maximum read range to estimate the target location. In [23], instead of determining the location of RFID tags, it attempts to determine the location of an RFID reader by analyzing the received radio signals transmitted from a distributed set of passive tags.…”

Section: Related Workmentioning

confidence: 99%

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

Alma'aitah

Alsaify

Bani-Hani

2019

Sensors

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Small and pervasive devices have been increasingly used to identify and track objects automatically. Consequently, several low-cost localization schemes have been proposed in the literature based on angle of arrival (AoA), time difference of arrival (TDoA), received signal strength indicator (RSSI) or their combinations. In this paper, we propose a three-dimensional empirical AoA localization (TDEAL) technique for battery-powered devices. The proposed technique processes the AoA measurements at fixed reader nodes to estimate the locations of the tags. The proposed technique provides localization accuracy that mitigates non-linear empirical errors in AoA measurements. We utilize two omni-directional antenna arrays at each fixed reader node to estimate the location vector. With multiple location estimations from different fixed reader nodes, each estimated location is assigned a weight that is inversely proportional to the AoA phase-difference error. Furthermore, the actual AoA parabolic formula of the location is approximated to a cone to simplify the location calculation process. The proposed localization technique has a low hardware cost, low computational requirements, and precise location estimates. Based on the performance evaluation, significant location accuracy is achieved by TDEAL; where, for instance, an average error margin of less than 13 cm is achieved using 10 readers in an area of 10 m × 10 m . TDEAL can be utilized to provide reference points when integrated with a relative (e.g., inertial navigation systems) localization systems.

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Indoor distance estimation for passive UHF RFID tag based on RSSI and RCS

Cited by 52 publications

References 23 publications

Research challenges in Measurement for Internet of Things systems

Research challenges in Measurement for Internet of Things systems

Indoor Localization Systems for Passive Uhf Rfid Tag Based on Rssi Radio Map Database

Three-Dimensional Empirical AoA Localization Technique for Indoor Applications

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