Abstract-Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. 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. In this work, we 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 build a propagation model and a fingerprinting system at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

Abstract-Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. WiFi-based indoor localization has been attractive due to its open access and low cost properties. 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 is a big challenge. In this work, we 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 effect at the receiver. We implement the FILA system on commercial 802.11 NICs, and then evaluate its performance in different typical indoor scenarios. The experimental results show that the accuracy and latency of distance calculation can be significantly enhanced by using CSI. Moreover, FILA can significantly improve the localization accuracy compared with the corresponding RSSI approach.

Abstract-WLAN-based indoor location fingerprinting has been attractive owing to the advantages of open access and high accuracy. Most fingerprinting-based systems so far rely on the received signal strength (RSS), which can be easily measured at the receiver with commercial WLAN equipment. However, RSS is a coarse value which simply measures the received power for a whole channel. Thus, it fluctuates over time in typical indoor environments with rich multipath effects and not unique for a specific location. In this paper, we present the design, implementation, and evaluation of a Fine-grained Indoor Fingerprinting System (FIFS). FIFS explores a PHYlayer Channel State Information (CSI) that specifies the channel status over all the subcarriers for location fingerprinting in WLAN. The system leverages the CSI values including different amplitudes and phases at multiple propagation paths, known as the frequency diversity, to uniquely manifest a location. Moreover, the multiple antennas provides the spatial diversity that can be further augmented in fingerprinting. We also present a coherence bandwidth-enhanced probability algorithm with a correlation filter to map object to the fingerprints. We conducted experiments in two typical indoor scenarios with commercial IEEE 802.11 NICs. The experimental results demonstrate that the overall positioning accuracy can be improved compared with the RSS-based Horus system.