IEEE 802.11 WLAN based Indoor Positioning Algorithm using Weight Grey Prediction Model

The accuracy and stability of indoor location method based on received signal strength indication (RSSI) decrease due to multipath effect of wireless signal. With the development of Wi-Fi technology, the IEEE 802.11n series communication protocol and the subsequent wireless LAN protocols use multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) and other technologies. WI-FI using CSI technology has emerged as a new paradigm of indoor positioning service (IPS). However, the propagation of RSSI and CSI is still affected by indoor multiple paths, and we cannot obtain signals in some corner areas. Therefore, RSSI and CSI need a good calibration method to improve the accuracy of the position estimation system. In this paper, we propose a RSSI and CSI based indoor positioning method. In this method, CSI of physical layer combined with RSSI is used as reference information to reduce multipath attenuation of signal receiver. On this basis, the weighted gray prediction model is used for data processing to reduce the time variability of the received signal. Finally, this method combines with trilateration algorithm to further reduce positioning error. This paper also provides experimental comparisons of our proposed data generation method with existing indoor positioning methods. Experimental results show that the proposed data generation method can significantly improve the positioning accuracy, reduce the computational complexity, and have a better indoor positioning effect than using an effective CSI positioning method. Meanwhile, the proposed method also can obtain more accurate positioning results in corner areas of the indoor environment where WIFI signals cannot be obtained.