Bluetooth Indoor Positioning Based on RSSI and Kalman Filter

In recent years, indoor positioning is becoming more and more important. Satellites can position only in the outdoor environment, which is unable to achieve precise positioning in the indoor environment. At present, the indoor positioning is mainly based on wireless signals, such as WiFi, RFID, Zigbee, Bluetooth etc. The cost and power consumption of using WiFi, RFID and Zigbee to realize the indoor positioning is very high and the deployment of WiFi, RFID and Zigbee is inconvenient. In this paper,indoor positioning is based on Bluetooth ibeacon, which is Bluetooth 4.0 standard. The power consumption and the cost of Bluetooth 4.0 is lower than others. In addition, Bluetooth has spread widely in the distance. This paper proposes a new indoor location method, which uses the method of learning to train the Bluetooth signal propagation model in the museum environment and uses the method of weighted least square and four-border positioning to estimate the location of the target object. The experimental result shows that the method is stable and good robustness. The positioning accuracy meets the requirements of the indoor positioning.