Detección Robusta de Tags RFID usando Hidden Markov Models
Hidden Markov Models (HMMs) have proven to be powerful tools for modeling sequential data across various applications. This paper explores their use in RFID-based tag detection, focusing on improving accuracy and robustness in environments affected by signal noise and interference. We propose a meth...
| Autores: | , , |
|---|---|
| Formato: | capítulo de livro |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | español |
| OAI Identifier: | oai:dnet:uabarcelona_::e982501c8b647358621f55b6eab6d49a |
| Acesso em linha: | https://ddd.uab.cat/record/328334 |
| Access Level: | acceso abierto |
| Palavra-chave: | Hidden Markov Models RFID |
| Resumo: | Hidden Markov Models (HMMs) have proven to be powerful tools for modeling sequential data across various applications. This paper explores their use in RFID-based tag detection, focusing on improving accuracy and robustness in environments affected by signal noise and interference. We propose a methodology that leverages the probabilistic nature of HMMs to classify and predict RFID tag readings, thereby enhancing detection reliability. The effectiveness of the approach is evaluated through simulations, demonstrating its potential for improving modern RFID reader performance. Our results indicate that HMMs can significantly improve detection precision, providing a more reliable solution for RFID applications. Additionally, we analyze the impact of model parameters and propose optimizations to enhance performance in dynamic scenarios. This research is a first step towards improved RFID systems by integrating probabilistic model. |
|---|