The Research Of RFID Indoor Positioning System Based On Improved LANDMARC Algorithm

In recent years,the rapid development of communication technology and wireless sensor technology driving the Internet of things technology gradually rise,wireless location technology as an important research field of Internet of things technology come under more and more attention,and indoor positioning technology is used widely by the researchers.RFID has the advantages of non-line of sight,high precision,short time delay,low cost and large transmission range,which has promoted it to the main solution of indoor location problem.At present,the two-dimensional positioning algorithm based on RFID technology has matured.However,the existing research results for three-dimensional space can't meet the increasing demand for applications.Therefore,based on the analysis of the 3D-LANDMARC positioning system's mathematical models and related technologies,we can improve the positioning performance of the system by optimizing the positioning process while keeping the system's equipment unchanged.Aiming at the problem of standard LANDMARC positioning algorithm is not high positioning precision and strong dependence on reference tag because of its own layout and operation principle.In this paper,we first use the BP neural network which has better performance of data fitting to build the path loss model of wireless signal in the current indoor environment,so as to improve the accuracy of signal transmission distance estimation.Then,the problem of estimating the coordinate of the label under test in the LANDMARC positioning system is transformed into the problem of minimizing the error between the estimated distance and the measured distance between the label under test and the reference label.A new swarm intelligence optimization algorithm is formed by incorporating quantum particle swarm optimization into the framework of cultural algorithms.The intelligent optimal ability of the algorithm is used to approximate the optimal solution as the final coordinate estimate.Finally,the simulation is carried out on the MATALB platform.The experimental results show that the proposed algorithm improves the positioning performance to meet the expected requirements.Aiming at the problem of the large positioning error of LANDMARC positioning system affected by indoor environment disturbance and system noise,this paper firstly introduces nonlinear filtering algorithm into LANDMARC positioning system and the space model of the nonlinear filtering algorithm is given under the framework of the LANDMARC positioning system.Then,aiming at the situation that CKF algorithm reduces the filtering performance when the environment changes,the quantum particle swarm optimization algorithm is introduced into the updating process of CKF algorithm to improve the accuracy of filter state estimation.Finally,through simulation experiments in MATLAB environment,we can see that the improved algorithm proposed in this paper has higher positioning accuracy and stability compared with the traditional positioning system.