Wireless Indoor Positioning Technology Research Based On The Received Signal Strength

In recent years, with the development of intelligent devices, a series of applications related to the location information have got more and more attention and concern from people day by day. Global positioning system(GPS) has been successfully applied in the outdoor positioning, and how to obtain the location information in complex indoor environment becomes the urgent need in people’s daily life. However, due to the complexity of the propagation characteristics of the wireless signal in the indoor environment, the existing positioning technologies are widely applied in outdoor environment cannot meet the requirements of the indoor application. That prompts a large number of scholars do a lot of research on the application of wireless location technology in indoor environment, which makes the rapid development of indoor positioning technology. And many location based services such as asset management, personnel positioning, information pushing and so on have been developed.Compared with the outdoor environment, indoor environment is more complicated. The wireless signal is affected by non line-of-sight, reflection, multi-path, and other factors, and the existing indoor positioning systems make it difficult to meet the requirements of high precision and low cost. At present, the Zigbee protocol has become a hot research topic in the indoor positioning system due to its low power consumption, low cost, short time delay, and the advantages of high capacity, high security.This paper uses the Zigbee wireless sensor network and studies the kalman filtering algorithm in the application of Zigbee indoor location tracking system. In this paper, we deploy the Zigbee nodes in the actual indoor environment to collect the received signal strength(RSS) data, modeling the indoor wireless signal propagation characteristics. The experimental results show that the SS information in the same position has very great volatility in different time, then using the extended kalman filter algorithm solves the large observation error in observation model. Because this article only uses the signal strength information, a lot of information related to the channel state and the signal characteristics is underutilized. Considering the spatial structure relations, using two mobile nodes(or more) to constitute an array has been proposed in this paper, which avoids the weaknesses of the only a single node that uses the less information. Then use a mobile node information to assist another node to complete the positioning. Through the extended kalman filter(EKF) implements information fusion in order to gain higher positioning accuracy. It has verified the effectiveness of the proposed scheme to compare with the other location tracking methods.By computer simulations and practical physical platform test, the node auxiliary scheme proposed in this paper can improve the position tracking accuracy to a certain extent, which has a certain theoretical reference and practical application value.