Abstract
The existing active tag-based radio frequency identification (RFID) localization techniques show low accuracy in practical applications. To address such problems, we propose a chaotic adaptive genetic algorithm to align the passive tag arrays. We use chaotic sequences to generate the intersection points, the weakest single point intersection is used to ensure the convergence accuracy of the algorithm while avoiding the optimization jitter problem. Meanwhile, to avoid the problem of slow convergence and immature convergence of the algorithm caused by the weakening of individual competition at a later stage, we use adaptive rate of change to improve the optimization efficiency. In addition, to remove signal noise and outliers, we preprocess the data using Gaussian filtering. Experimental results demonstrate that the proposed algorithm achieves higher localization accuracy and improves the convergence speed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
