Particle Swarm Optimization in SAR-Based Method Enabling Real-Time 3D Positioning of UHF-RFID Tags

Bernardini, Fabio; Buffi, Alice; Motroni, Andrea; Nepa, Paolo; Tellini, Bernardo; Tripicchio, Paolo; Unetti, Matteo

doi:10.1109/jrfid.2020.3005351

Cited by 53 publications

(20 citation statements)

References 44 publications

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“…As discussed in the Introduction, different RFID-based localisation systems were designed in the literature. Although some solutions rely on the combination of phase and amplitude measurements [ 26 , 33 ], phase-only measurements prove to be quite effective in indoor environments [ 21 ]. To account for the phase ambiguity, different filters were provided that are based on the fusion with wheel encoders, ranging from Unscented Kalman Filters (UKF) [ 37 ], Multi-Hypothesis EKF [ 32 ] and particle filters [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, the signal phase is by far more robust and effective for localisation purposes especially in indoor scenarios [ 21 ]. The main challenge to be solved is the ambiguity of phase measurements due to the

-periodicity [ 28 ], and the knowledge of the phase offset term.…”

Section: Related Workmentioning

confidence: 99%

“…In harsh industrial scenarios with lots of metallic structures, Commercial-Off-The-Shelf (COTS) on metal tags can be employed. Finally, when the vehicle is natively equipped with COTS RFID hardware, e.g., the RFID-robot employed for inventory tasks [ 21 ], the implementation of the RFID-based localisation infrastructure amounts just to reference tag deployment.…”

Section: Introductionmentioning

confidence: 99%

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Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Shamsfakhr

Motroni

Palopoli

et al. 2021

Sensors

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Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

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Section: Discussionmentioning

confidence: 99%

-periodicity [ 28 ], and the knowledge of the phase offset term.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Shamsfakhr

Motroni

Palopoli

et al. 2021

Sensors

Self Cite

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“…In [20]- [23] techniques for safe operation of drones have been discussed, and in [24]- [28], routing techniques for drones have been presented. In [29] and [30], synthetic aperture (SAR) has been applied to identify RFID tag-affixed objects in a warehouse. In addition, in drone-based inventory management systems, compact RFID reader antennas are in demand because the drone needs to carry the RFID reader.…”

Section: Introductionmentioning

confidence: 99%

“…This article presents a framework, which was evaluated experimentally, to determine the secure distance. As we mentioned before, [1]- [5] developed practical drones and robot-based systems, and [11]- [13] and [30] studied electromagnetic propagation effects and the use of SAR techniques. Although many inventory management systems based on drones and RFID systems have been proposed to date, this article establishes the framework to determine the secure distance.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Determine Secure Distances for Either Drones or Robots Based Inventory Management Systems

et al. 2020

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We propose a framework to determine a secure distance between a drone with an ultrahigh-frequency band radio frequency identification (UHF band RFID) reader and metallic objects affixed with RFID tags. The secure distance avoids order changes in received signal strength indicator (RSSI) values among the identified RFID tags in the field of view of the RFID reader. This distance enables a drone operator to securely operate the drone while identifying the RFID tag on the front of an object based on the measurements of RSSI values. An RFID tag located on the front of an object provides the maximum RSSI value. However, multipath propagation alters the RSSI values. Therefore, a framework is needed to determine a secure distance considering the multipath effects. Although inventory management systems based on drones and RFID systems have been proposed to date, this article establishes a framework to determine the secure distance. In the proposed framework, RFID tag and reader radiation patterns and multipath propagation effects were considered. The proposed framework was evaluated theoretically and experimentally. To evaluate and demonstrate the secure distance, we measured the RSSI values of two RFID tags attached to a metallic balcony. The height from the ground and spacing of the two RFID tags were 1.5 m and 1.3 m, respectively. In this environment, the secure distance was 3.8 m. The experimentally obtained distance that avoids order changes in RSSI values corresponded well with that obtained by this framework. The proposed secure distance is crucial when either drones or robots are introduced to inventory management systems.

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Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID

Mathavan,

Siva Ranjani,

Suresh

et al. 2024

Int J Communication

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SummaryIn an indoor setting, the radio frequency communication is utilized to locate and track mobile objects utilizing Radio Frequency Identification (RFID) technology. Measurements from the Received Signal Strength Indicator (RSSI) are typically the foundation of the localization technique. In an RFID‐based interior setting, lowering tracking mistakes and increasing the precision of tracking remain difficult tasks. In order to address these issues, we developed the VIRALTRACK (Virtual Reference Tag Localization and Tracking) framework that consists of four procedures: deep reinforced learning‐based tracking, quantum‐based localization, optimization‐based virtual reference tag allocation, and signal enhancement. In order to increase the signal's effectiveness, we initially suggested using the Extended Gradient Filter (EGF) technique to eliminate RSSI oscillations. In the second step, we suggested using the Emperor Penguin Colony (EPC) optimization technique to allocate the virtual reference tag while taking the number of tags, SNR, and temperature and humidity of the surroundings into account. In the third phase, we use a quantum neural network (QNN) for localization in order to estimate the position of the moving target. We introduced the SignRank approach to select the best virtual reference tag for localization, which lowers tracking mistakes. In conclusion, we presented the Twin Delayed Deep Deterministic Policy Gradient (TD3) method that boosts the tracking precision by tracking the moving target tag efficiently and taking into account stage, the orientation, distance, and valuable coordinates. The NS3.26 network simulator is used to run the simulation, and tracking precision, tracking error, and accumulated probabilities are used to assess effectiveness.

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Particle Swarm Optimization in SAR-Based Method Enabling Real-Time 3D Positioning of UHF-RFID Tags

Cited by 53 publications

References 44 publications

Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

A Framework to Determine Secure Distances for Either Drones or Robots Based Inventory Management Systems

Application of optimization algorithm for virtual reference tag assisted localization and tracking of RFID

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