FIFS: Fine-grained Indoor Fingerprinting System Jiang Xiao, Kaishun Wu, Youwen Yi and Lionel M. Ni Department of Computer Science and Engineering Hong Kong University of Science and Technology Email: {jxiao, kwinson, ywyi, ni}@cse.ust.hk 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 PHY- layer 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. I. I NTRODUCTION The advance of wireless technology has fostered the flourish of indoor location-aware applications, such as indoor naviga- tion, warehouse management and health care, etc. With the ad- vent of wireless communications, wireless local area networks (WLANs) that increasingly being deployed in offices and homes recently become a means of wireless indoor localization technique. Due to the open access and low cost properties, it opens an opportunity for leveraging the existing WLAN IEEE 802.11 [1] infrastructure to provide precise location estimation in indoor environment. Many WLAN-based indoor positioning systems [2], [3], [16] adopting ”fingerprinting” technique have gain popularity due to higher accuracy. The fingerprinting- based approaches typically determine the location based on two phases: first, associating location-dependent characteris- tics to certain locations for constructing a radio map (offline training phase); then, mapping the characteristic of the object to the radio map to infer the location (online positioning phase). Radio signal strength (RSS) is widely used in fingerprinting positioning systems to signify the location-dependent char- acteristic, such as RADAR [4] and Horus [5]. The striking point of RSS-based fingerprinting lies in the simplicity of deployment with no specialized hardware required at the mobile station except the wireless network interface card (NIC). However, we claim that the weakness of RSS-based fingerprint stems from two aspects: First, RSS varies with time at a fixed position [5] due to the multipath effects, which including the reflection, diffraction and diffusion in indoor environments. Second, RSS is a coarse measurement of the received power at the radio frequency band. For several locations, the RSS values may be reproducible because it lacks of the frequency information to capture the multipath property. Therefore, this time-varying and duplicated RSS value describes signal characteristics inaccurately and creates undesirable localization errors. We argue that a reliable metric provided by commercial NICs to improve the accuracy of indoor localization is in need. Such metric should be more temporal stable and provide the capability to benefit from the multipath effect. In current wide- ly used Orthogonal Frequency Division Multiplexing (OFDM) systems, where data are modulated on multiple subcarriers in different frequencies and transmitted simultaneously, we have a notation from PHY layer that represents the channel properties over all the subcarriers called Channel State In- formation (CSI). The primary advantage of CSI over RSSI is that this fine-grained information estimates the channel on each subcarrier in the frequency domain. In contrast to only one RSSI per packet, we can obtain multiple CSI values at one time and the values stay fairly stable over time [8], [18]. In our pervious work [18], we have built a propagation model based on CSI. And then we use it for precise indoor localization by eliminating the multipath effect. However, we observe that CSIs over multi-subcarrier will have unique signatures in different locations. These unique features come from the frequency diversity of CSI, which has different amplitudes and phases of each subcarrier. By exploiting the frequency diversity, we can construct a unique “fingerprinting” indicating each location on the radio map. Motivated by this, it is favorable to leverage the CSI for location fingerprinting and thus improve the localization accuracy. Moreover, MIMO technique that exploits the space dimen- sion to improve capacity, range and reliability of wireless systems is widely applied nowadays (e.g., 802.11n, WiMax, 3GPP LTE, etc.). The authors in [6] investigated the impact of applying multiple antennas on indoor location systems. By employing multiple antennas, the signal strength variability can be reduced due to small scale fading compensation. Like- wise, the stability of a localization system can be enhanced. Since the commercial 802.11n NICs in the current market are mostly equipped with multiple antennas, the instinctive spatial