Intelligent RFID-Based Localization and Tracking Model Design Using Unscented Kalman Filter and Convolution Neural Network-Reference-Cited by-同舟云学术

Intelligent RFID-Based Localization and Tracking Model Design Using Unscented Kalman Filter and Convolution Neural Network

Published:2022-01-20 Issue: Volume: Page:
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Container-title:2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT)
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Author:

Soni Noopur¹,Mishra Agya¹

Affiliation:

1. Jabalpur Engineering College,Department of Electronics And Telecommunication,Jabalpur,Madhya Pradesh,India,482011

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9716239/9716227/09716381.pdf?arnumber=9716381

Reference20 articles.

1. RFID Reader Planning for the Surveillance of Predictable Mobile Objects;weiping;2017 International Conference on Identification Information and Knowledge in the Internet of Things,0

2. Hierarchical multiobjective approach for optimising RFID reader deployment

3. A Standalone RFID Indoor Positioning System Using Passive Tags

4. Multi-objective RFID network planning with probabilistic coverage model by guided fireworks algorithm

5. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

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1. 3D Internet of Things Accurately Localizes RFID Devices Using RSSI for Optimal Routing;2023 International Conference on Data Science and Network Security (ICDSNS);2023-07-28

2. Accurate RF-sensing of complex gestures using RFID with variable phase-profiles;2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE);2023-06-19

3. eDeepRFID-IPS: Enhanced RFID Indoor Positioning with Deep Learning for Internet of Things;Advanced Information Networking and Applications;2023

4. The Intuitive Supervision Model (ISM) using Convolution Neural Networks (CNN) and Unscented Kalman Filters (UKF);International Journal of Recent Technology and Engineering (IJRTE);2022-01-30