Research On RSSI Indoor Positioning Algorithm Based On RSSI

With the rapid development of technologies such as the Internet,cloud computing and intelligent terminal,people’s demand of Io T-related technologies is also increasing.Global Positioning System(GPS),base stations and other positioning methods are difficult to meet the needs of indoor location-based services such as indoor navigation,location tracking,smart home control,and intelligent logistics.Various indoor positioning methods using different techniques,such as infrared,Bluetooth,ultrasound,Wi-Fi,ultra-wide band(UWB),Zigbee,radio frequency identification(RFID)have been rapidly developed.Among them,the RFID technology has attracted considerable attention because of its advantages of low system deployment cost and strong system positioning performance.Due to the reflection,scattering,diffraction and other phenomena of obstacles,when the RF signal propagates indoors,the multipath effect will result in positioning error,and therefore the existing RFID indoor positioning techniques have the problems of poor environmental adaptability and low positioning accuracy.In this thesis,RFID indoor positioning technology is studied to address the above problems in two-dimensional and three-dimensional positioning scenarios,respectively,and the main work of this thesis is summarized as follows:(1)In the two-dimensional scenario,aiming at the problems of the traditional RFID positioning algorithm based on path loss model being greatly affected by the environment and the low positioning accuracy,this thesis proposes an RFID indoor positioning algorithm based on Radial Basis Function(RBF)neural network model and Unscented Kalman Filter(UKF).In the proposed RBF-UKF positioning method,by establishing a RBF neural network model,the Received Signal Strength Indicator(RSSI)measured by the reader is used as the input of the RBF neural network,and the path loss coefficient and RSSI values of the output are used as the input of the UKF algorithm.The initial position coordinates obtained by the least squares method are optimized,which effectively decreases the positioning error caused by the change of indoor environment.Simulation results show that compared with other existing algorithms,the proposed algorithm can effectively improve the system’s robustness and positioning accuracy.(2)In the three-dimensional scene,in order to improve the search ability of Harris Hawk Optimization(HHO)algorithm in RFID indoor positioning segmentation space,this thesis proposes an RFID indoor positioning algorithm based on the improved Harris Hawk optimization(IHHO)method.In the proposed IHHO positioning method,the reference label is placed at the top and side of the measured space,and the distance from the tag to the reader is obtained according to the RSSI value received by the reader.Then,the space division is carried out to preliminarily determine the area affiliated with the target label.Finally,the optimal value of the objective function is calculated by using the IHHO algorithm in this region,and is used as the final estimated position of the target label.Simulation results show that compared with other existing algorithms,the proposed IHHO positioning method has better stability and positioning accuracy.