Research On Indoor Positioning Methods Based On RFID Tags

With the development of Internet of things technology,people pay more and more attention to the positioning technology in radio frequency identification(RFID).In the process of location information identification,if the tag positioning problem is not well solved,it will affect the efficiency of work and life.In order to speed up the processing of location information identification,the design of RFID algorithm for tag positioning is particularly important.At present,there are a large number of indoor positioning technologies,such as WiFi,Bluetooth,infrared,ultrasonic,radio frequency identification and other indoor positioning technology.Among them,RFID positioning technology is widely used in indoor positioning with the advantages of low tag cost and strong positioning performance of the system.Therefore,the artical chooses the tag positioning in RFID as the research topic.In this artical,the radio frequency identification indoor positioning algorithm is studied from two aspects.On the one hand,we design a indoor positioning algorithm based on multi-direction search.On the other hand,we design a indoor positioning algorithm based on adaptive learning factor.The details are as follows.(1)Design a indoor positioning algorithm based on multi-direction search.In order to improve the accuracy of location information in RFID system,this artical proposes a multi-direction target search positioning algorithm.Using the reference of hexagon tags deployment environment,starting from the center of the search area,searching in the direction of the reference tags according to the set step size,each of the search point is regarded as a virtual tag in the search area,through the signal strength of each virtual tag euclidean distance to determine the next search direction,when meet the setting threshold value,the search ends.The multi-direction target search algorithm solves the problem that the traditional LANDMARC algorithm fails to select the nearest neighbor reference tags.The algorithm not only reduces the influence of multi-path interference,but also makes the position error smaller and the search result more accurate.Experimental results show that the positioning error of the proposed multi-direction search algorithm is reduced by 0.47 m compared with LANDMARC algorithm and 0.11 m compared with VIRE algorithm.(2)Design a indoor positioning algorithm based on adaptive learning factor.Based on the traditional particle swarm optimization algorithm,the method of assigning values to learning factors is eliminated,and a dynamic learning factor is proposed,which is a factor formula varying with the number of iterations.Then,the particle swarm optimization algorithm with adaptive learning factor is applied in RFID positioning,and each randomly generated particle isregarded as an independent virtual tag.The signal strength value of the virtual tag is obtained by interpolation.The fitness function is established through the signal strength relationship between the tag to be tested and the particle,and the search direction is determined by the fitness function value.Finally,When the number of iterations reaches the maximum value,the search ends.The dynamic learning factor can not only expand the range of tag search in the early stage of the algorithm,but also easily find the best position of the tag to be tested later in the algorithm.Experimental results show that compared with the traditional positioning algorithm,the proposed algorithm based on adaptive learning factor reduces the positioning error by 0.54m.