Research On WSN Indoor Wireless Position Technology Based On ZigBee

Micro-Electro-Mechanical System (MEMS), System on Chip (SOC), wireless communication and sensor technology are included in Wireless Sensor Network (WSN). With the rapid development of these technologies, Wireless Sensor Network has become a new research hotspot. Wireless Sensor Network has extensive application prospects in many fields, such as military defense, environmental monitoring, medical attention, Building condition monitoring and so on. And indoor wireless position is also an important application in Wireless Sensor Network. As the indoor wireless environment is complex, which includes large interference, multi-path effects caused by reflection, diffraction, refraction and scattering and serious fading caused by the movement of people, indoor position is more difficult than other location technology. At present, indoor wireless position and relevant algorithm has become hot research spot, and how to improve accuracy of location will be a key research point in future.Firstly, Wireless Sensor Network, ZigBee and some common indoor position technologies are introduced, and then indoor location technology based on RSSI (Received Signal Strength Indicator) and relevant algorithms are introduced primarily in this thesis. We introduce some classic location algorithms based on RSSI, and analyze the performance, advantages and disadvantages of these algorithms. By analyzing the advantages and disadvantages of weighted centric algorithm and least square method algorithm, a new hybrid algorithm is proposed. In this algorithm, a dynamic real-time method of solving path fading factor is proposed, which improves accuracy of RSSI ranging to some extent; then a modified algorithm based on weighted centric algorithm is proposed, which improves accuracy of location estimation, and we simulate the algorithm with MATLAB. In the simulation, the new algorithm which we propose is compared with weighted centric algorithm and least square method algorithm, and the statistical error data in the different simulation conditions is obtained. The simulation data indicate that, the new algorithm is better than other common algorithms in precision.