Compensating Signal Loss in RFID-Based Localization Systems

Modeer, Marina Rantanen; Vette, Stephan; Engell, Sebastian

doi:10.1016/j.ifacol.2019.08.062

Cited by 4 publications

(1 citation statement)

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“…Nevertheless, the K‐NN algorithm's early parameter choice is challenging, which makes indoor placement challenging. Modeer et al 25 suggest using an RFID system that utilizes correcting signal loss to locate shifting items in the surrounding area. In this case, the filter is applied to the data collected to reduce uncertainty.…”

Section: Review Of Literaturementioning

confidence: 99%

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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Section: Review Of Literaturementioning

confidence: 99%