Improved adaptive genetic algorithm based RFID positioning
Li Yu, Honglan Wu, Youchao Sun
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.