A novel 3D position measurement and structure prediction method for RFID tag group based on deep belief network

Abstract

In the influencing factors of reading distance of RFID tags, the tags’ geometric spatial structures have an important aspect on the corresponding reading distance. In order to achieve the goal of improving the reading distance of RFID tags, a novel three-dimensional (3D) position measurement and structure prediction method for RFID tag group based on deep belief network (DBN) is proposed. First, a 3D structure prediction system for RFID tags, which is based on stereovision, is designed. Second, an image deblurring scheme which combines the knife-edge with Wiener filtering method is adopted to restore the blur image in the 3D position measurement process. Then, RFID tags’ 3D positions are acquired by using the image matching method. Finally, the relationship model between RFID tags’ 3D positions and reading distance is built by DBN. The experimental results show that the relationship between tag group’s 3D positions and reading distance can be well built by DBN. The established DBN can forecast the reading distance accurately. The MAPE and RMSE of DBN prediction results are 1.64% and 0.168. The DBN can find the best 3D spatial distribution structure of RFID tag group corresponding to the maximum reading distance, which can guide the 3D distribution of the tag group.