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- EM Simulation-Driven Forward Modeling for RFID Tag LocalizationPublication . Ferreira, Francisco Bernardo Mota; Gomes, Hugo Miguel Cravo; Mendes, Luís Miguel MoreiraTag localization problems are addressed usually either through calculations where signal features such as received signal strength indicator (RSSI), phase, and time of flight (ToF) are derived solely from analytical expressions, or through field experiments using commercial off-the-shelf (COTS) readers. While the first approach often produces oversimplified and unrealistic results, the latter requires full system deployment in the target environment, which can be costly and time-consuming. This work explores a middle ground by leveraging electromagnetic (EM) simulations. Such simulations make it possible to extract realistic signal features - RSSI, phase, and ToF - directly from a modeled environment that closely resembles a realworld indoor RFID deployment scenarios. In the context of this work, a logistics conveyor scenario was modeled and used to assess the utility of EM simulations for determining the locations of tagged parcels. Particular emphasis was placed on analyzing the limitations and requirements of each positioning technique to ensure accurate operation. In addition to traditional range-based techniques, this work also studies range-free techniques that employ artificial intelligence for tag position estimation, evaluating how their performance compares to range-based approaches. Bilateration algorithm is employed with intensity-, phase-, time-based distance estimation techniques. Achieving high accuracy, with positioning errors below 15 cm. Phase- and, time-based techniques provided the most robust and consistent results. Although intensity-based approach was capable of achieving comparable accuracy under controlled conditions, it required prior knowledge of tag orientation to maintain this performance and demonstrated higher sensitivity to antenna radiation variation, leading to overall less consistent results. In contrast, the fingerprinting approach was evaluated using two deep neural network (DNN) models, but both exhibited lower performance, with error of 38 cm and 42 cm, likely due to the limited dataset with the coarse spatial sampling.