Indoor positioning systems have received increased attention for supporting location-based services in indoor and campus areas. Due to the open access and low cost property, location techniques based on widely-deployed wireless local area network (WLAN) are attractive. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance becomes a big challenge. In this work, we detailed analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subc...[ Read more ]

Indoor positioning systems have received increased attention for supporting location-based services in indoor and campus areas. Due to the open access and low cost property, location techniques based on widely-deployed wireless local area network (WLAN) are attractive. However, the distance estimation based on received signal strength indicator (RSSI) is easily affected by the temporal and spatial variance due to the multipath effect, which contributes to most of the estimation errors in current systems. How to eliminate such effect so as to enhance the indoor localization performance becomes a big challenge. In this work, we detailed analyze this effect across the physical layer and account for the undesirable RSSI readings being reported. We explore the frequency diversity of the subcarriers in OFDM systems and propose a novel approach called FILA, which leverages the channel state information (CSI) to alleviate multipath effects at the receiver. In FILA, we define an effective CSI value to represent the CSI of multiple subcarriers in a single packet, and propose a refined indoor radio propagation model on the relationship between CSI and distance. Finally, we apply the trilateration method to obtain the coordinates of the target client.

We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in three typical indoor scenarios. The experiments demonstrate proof-of-concept for the fine-grained CSI value. The results show that the accuracy and speed of distance calculation can be significantly enhanced using CSI. Moreover, FILA can improve the localization accuracy by up to 10 times compared with the existing RSSI approaches.