Research On Indoor Wireless Positioning And Tracking Technology Based On RSSI

In recent years, with the rapid development of mobile communication systems, indoor positioning technology based on wireless infrastructure construction has been widely deployed in many scenarios such as security and protection, social public services, and medical treatment & public health etc. With the increasing demand for indoor wireless positioning, how to get precise indoor positions has become an important direction of current research, which provides opportunities for wireless local area network(WLAN) technology for indoor wireless positioning. WLAN technology has obvious advantages in indoor wireless positioning, such as low cost, easy installation, wide coverage, strong scalability, and wide distribution of mobile terminals. Meanwhile, it also faces enormous challenges, the biggest of which is that it can be easily affected by environmental factors and interference, resulting in poor positioning accuracy. Therefore, this paper focuses on indoor wireless positioning and tracking technology based on the received signal strength indicator(RSSI) in WLAN environment.Firstly, research background and practical significance of the RSSI-based indoor wireless positioning are introduced in this paper. Then the evolution and difficult problems of existing indoor wireless positioning and tracking technology based on RSSI are summarized and the research direction is determined. Traditional Positioning algorithms usually assume a fixed position for anchor node, but the position will change in a lot of rapid deployment scenarios. Based on the maximum likelihood algorithm, a new positioning algorithm is proposed, which dynamically adjust the parameters of path loss model. In this algorithm, the positioning environment is firstly measured using the RSSI information between all the anchors participating in the positioning process. Then estimation and correction of the path-loss-model's parameters are made. Finally, using the modified channel model, the distances between the target nodes and anchor nodes are determined, so as the position of the target nodes. The simulation results show that the proposed method can effectively suppress the impact of environmental changing on the positioning accuracy, compared to the existing model parameter estimation algorithm. Meanwhile the positioning error is lower and performance is better.Moreover, at the locating and tracking stage, existing tracking algorithm based on Kalman filter has certain system limitations and the algorithm based on particle filter is easily effected by singular values. In view of this, a new tracking algorithm is proposed, which joint particle filter and Kalman filter. In this algorithm, the station of the target node is firstly evaluated using particle filter to overcome the impact of system limitation. Then the evaluations is filtered by Kalman filter to eliminate the impact of singular values, improve the tracking accuracy and track the target node precisely. Simulation results show that the proposed algorithm can adapt to a variety of sports scene, and it has lower error, higher stability, and stronger practical applicability, compared with the existing locating and tracking algorithm.