Research And Implement Of Indoor Positioning Technology Based On RSSI

With the rapid development of the Internet and intelligent terminals, indoor target positioning based on wireless sensor network has a wide range of application requirements, such as firefighters in the fire locomotive trajectory positioning. Based on the received signal strength indicator (RSSI), the WiFi node location technology has been widely used in indoor positioning because of its low cost, low power consumption and low hardware requirement. However, the complexity of the indoor environment, node transmission power, long-distance communication, sudden interference, environmental factors and other factors seriously affect the RSSI value of the measurement, thus affecting the positioning. Therefore, how to suppress the interference of indoor environmental factors, improve the accuracy of indoor positioning based on the RSSI range of WiFi indoor location is of great significance.In the traditional ranging system, the signal will be affected by interference factors such as multipath effect, obstacle and other noise during the propagation process, rand the RSSI value received by the beacon nodes at the same transmission distance will large differences, if the direct use this data is calculated, it will cause a greater error, reduce the conversion accuracy, so the need to collect the data processing. In this paper, the Kalman filter is used to preprocess the measured data to obtain the optimal estimate of the state vector. The least square method is used to fit the propagation equation. Based on the traditional UKF algorithm, an improved UKF algorithm based on logarithmic robust function is proposed. Using the robust correction technique to dynamically correct the variance of the process noise variance, improve the accuracy of the state variable estimation, make the secondary processing of the measured data, obtain the distance from the unknown node to the anchor node, reduces the influence caused by the non-line-of-sight environment,reduces the ranging error, obtains the coordinates of the unknown node by the trilateral positioning method. The particle swarm optimization algorithm is optimized the estimated coordinates of unknown nodes,reduce the coordinate error and improves the accuracy of the indoor positioning algorithm.The simulation results and experimental results show that compared with the traditional distance finding algorithm, the method can effectively reduce the ranging error and improve the positioning accuracy of the indoor positioning system.