# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 1 Introduction  
The technical development of the Internet of Things (IoT) has enhanced people's demand for location services. In the IoT era, position information is crucial. Among these services, indoor positioning-as-a-service, closely related to people's lives, has attracted a great deal of attention. The indoor positioning system (IPS) can be widely used in home surveillance systems, nursing care for the elderly, and patient monitoring in hospitals [1]. Most existing surveillance systems use cameras for monitoring purposes [2]. However, these have insufficient coverage and blind areas. To improve the performance, it is necessary to increase the number of cameras. However, this also brings an excessive cost and complex configuration. Further, privacy is still a problem that cannot be ignored. To address these problems, various methods utilizing radio waves have been considered. By capturing the fluctuation of wireless communication channel characteristics due to a person's movement and applying machine learning, the estimation of a person's position and behavior pattern can be achieved. However, positioning in an indoor environment still has many technical challenges owing to different propagation conditions from outdoor applications [3]. For example, angle-of-arrival (AOA)-based localization has received great attention in the past decades. However, the major difficulty of AOA-based positioning is the influence of multipath propagation. The complexity of estimating the unknown channel parameters (amplitudes, delays, and angles) grows as the number of paths increases [4]. The time-of-arrival (TOA)-based system also has many challenges in obtaining high localization accuracy due to the difficulty of the order of one nanosecond measuring of the TOA using low-cost hardware in multipath-rich environments. Such systems suffer from time and frequency offsets between the local clocks in different nodes [5]. Furthermore, the range-based indoor positioning system is easily degraded in non-line-of-sight (NLOS) propagation environments [6]. In addition, the commonly used localization technologies such as the Global Positioning System (GPS) do not work effectively (due to low accuracy, a bad signal, and other issues) in an indoor environment owing to signal fading and multipath effects caused by building structures. Therefore, we focused on wireless devices that have already been widely deployed, such as WiFi (WLAN usingIEEE802.11 standards). WiFi is widely used in homes, hotels, cafes, airports, shopping malls, and other various types of large or small buildings, which makes it very attractive for indoor positioning applications. The universality and affordability of WiFi devices will enable the popularization of indoor localization systems. Recently, various studies researching indoor localization systems using WiFi devices have been reported. For instance, the IPS using the fingerprint method of received signal strength indication (RSSI) has been discussed [7, 8]. However, owing to the temporal and spatial variation of the indoor environment [9], several access points (APs) are required to improve the accuracy; moreover, the configuration would become complex [10]. Channel state information (CSI)-based IPS has been developed [11, 12, 13, 14]. However, owing to the limited bandwidth of WiFi devices, several APs are required to maintain the performance of the system [15]. Note that the latest WiFi standards allow a greater bandwidth, but no open-source tools for CSI acquisition have been provided. Therefore, we still need to use specific chipsets for CSI acquisition such as Qualcomm Atheros 9380/9580 and Intel 5300 [16], which support only IEEE 802.11n. Moreover, a microwave system using an antenna array has already been developed [17] where predetermined events are identified by monitoring the fluctuation of the signal subspace spanned by the eigenvectors.