# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 1 Introduction  
The term \"device-free localization\" (DFL) refers to the use of technologies which enable the automatic localization of human and/or nonhuman targets, without requiring the targets to carry a device or tag. While there is a wide variety of localization approaches which fall under the umbrella of DFL, ranging from differential air pressure-based techniques [1] to techniques which make use of floor vibrations [2], the term is often used in the context of Radio Frequency (RF)-based DFL. In this approach, the impact of the physical presence of targets on RF-waves within an environment is used to infer location-related information about them. The research domain of device-free localization was formally defined for the first time in 2007 by [PERSON] et al. [3]. They considered DFL to consist of primarily three aspects: detection, tracking and identification. Detection refers to determining whether there are any entities present causing changes in the environment and if so, how many entities there are. Tracking refers to estimating the actual locations of these entities and the manner in which these evolve over time. Finally, identification refers to determining identity-related information regarding these entities. The term \"identity\" in this contextis loosely defined and can refer to both physical characteristics (e.g., size, mass or main material) or to an actual human identity. This aspect in particular was considered to be exceptionally difficult and was referred to as \"the identification problem\". In the years since the aforementioned seminal study was published, significant advances have been made within this field of research. Many different techniques have been developed which focus on one or more of the three main aspects [4]. While initial approaches tended to solely make use of the impact of targets on the Received Signal Strength (RSS) of RF signals, an increasing number of studies is focused on other signal descriptors--most notably Channel State Information (CSI) [5]. CSI provides both amplitude and phase information for each subcarrier in RF communication which makes use of Orthogonal Frequency Division Multiplexing (OFDM). Its increasing use within DFL is primarily fueled by the increasing ease with which one can obtain this data from commercially available Wi-Fi equipment [6]. In recent years, CSI-based approaches have even led to the creation of identification systems which can differentiate between individuals based on the manner in which their unique gait leads to different measurement patterns [7; 8; 9]. It should be noted, however, that this assumes the availability of the necessary bandwidth for these signals. This is not feasible for every application and/or environment and purely RSS-based DFL systems are still an active topic of research [10; 11]. One popular approach which was already used by [PERSON] et al. [3] to illustrate the feasibility of DFL as a concept, is called passive radio mapping or passive fingerprinting. The basic concept of this method is similar to its active fingerprinting counterpart. A training-based radio map (or fingerprint database) is constructed which links possible target locations to corresponding sets of measurements. When the system is online, live measurements are compared to the fingerprint database and the closest matches are then assumed to be indicative of locations which are near the true target location. For the active approach, measurements based on RF links between a device attached to the target and stationary nodes installed within the environment are used. In contrast, passive fingerprinting makes use of the RF-communication between the stationary nodes, which is impacted differently based on the target location(s). Existing research shows that RSS-based passive fingerprinting systems can accurately locate human targets in large and complex environments with a surprisingly small number of stationary nodes.