The Intuitive Supervision Model (ISM) using Convolution Neural Networks (CNN) and Unscented Kalman Filters (UKF)

Author:

Soni Noopur, ,Mishra Dr. Agya,

Abstract

Radio frequency identification technology is one of the fastest-growing technologies in the realms of navigation, medical, robotics, communication system, logistics, security, safety, etc. Surveillance is one of the important fields where high accuracy and fast response are needed. In this research work, RFID sensors are used to track moving objects with an intelligent supervision model. The sophisticated surveillance model employs neural networks followed by an adaptive filtering technique based on an Unscented Kalman filter. A neural network is also one of the most efficient and powerful technology in the field of learning and data processing capability. A neural network has the capability of processing a mammoth amount of data because of this feature its efficiency and accuracy are quite high. This model localizes N number of objects/targets through an intelligent surveillance model, picks a random object from this pool of localized objects to track, categorizes their movement through a controlled checkpoint, and calculates the distance traveled by the moving object /target. Experimental results show that the proposed model can locate multiple-objects with the help of multiple input RFID antennas and tags and track them concerning to the RFID antennas with high accuracy and stability in the complex indoor environment and this intuitive model can be effectively implemented at the airport, railway station, shopping mall, retail management, as well as any other surveillance purpose. For this research work number of authors work, is reviewed and based on literature review this model is designed.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Subject

Management of Technology and Innovation,General Engineering

Reference18 articles.

1. Jitendra Damade and Agya Mishra, "Indoor RFID tracking System based on UKF Fusion Estimation Techniques" in CiiT International Journal of Digital Signal Processing, Vol. 9, No 7, August - September 2017 pp.129-134

2. Guillermo Alvarez-Narciandi, Andrea Motroni, Marcos R. Pino, Alice Buffi, Paolo Nepa, "A UHF-RFID gate control system based on a Convolution Neural Network" in 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA), 978-1-7281-0589-5/19/$31.00 ©2019 IEEE, pp 353-356.

3. Iyeyinka Damilola Olayanju, Olabode Paul Ojelab, "Using Multilateration and Extended Kalman Filter for Localization of RFID Passive Tag in NLOS" in Blekinge Institute of Technology February 2010, pp.1-49.

4. M. Truijens, X. Wang, H. de Graaf, and J. J. Liu, "Evaluating the Performance of Absolute RSSI Positioning Algorithm-Based Micro zoning and RFID in Construction Materials Tracking "in Hindawi Publishing Corporation Mathematical Problems in Engineering, Volume 2014, Article ID 784395, 8pages pp.1-8 http://dx.doi.org/10.1155/2014/784395

5. Nasir Kenarangui, "Real-Time Location Tool for Precision Tracking of Passive UHF RFID Tags in Two Dimensions" in the University Of Texas at Arlington August 2010,pp.1-70.

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