Indoor localization algorithm based on artificial neural network and radio-frequency identification reference tags

Abstract

With the development of Internet of Things technology, radio-frequency identification localization methods have been widely applied due to their low cost and ease of deployment. The indoor radio-frequency identification localization algorithm based on received signal strength indication technology is a currently hot topic. Because the received signal strength is highly dependent on environments, the classic algorithms may result in large errors in localization accuracy. This article proposed a new radio-frequency identification localization algorithm, named BP_LANDMARC, by utilizing the back propagation neural network, which is designed to address nonlinear changes in radio-frequency signals. A strategy for selecting different working parameters in variable environments is presented. The evaluation methods of root mean square error and cumulative distribution function are used to compare the proposed algorithm with some existing algorithms. Experimental results show that the proposed algorithm remarkably improves the localization accuracy of both absolute distance and cumulative probability. Moreover, the proposed algorithm performs effectively and efficiently when it is applied to a logistics warehouse management system.