# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment  
[PERSON]  
1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; [EMAIL_ADDRESS] (J.X. ); [EMAIL_ADDRESS] (N.C.)  
[PERSON]  
1 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; [EMAIL_ADDRESS] (J.X. ); [EMAIL_ADDRESS] (N.C.)  
[PERSON]  
2 School of Information Science and Engineering, Zazohu University, Zazohu, Z27160, China  
but it is expensive [4]. The first problem in the implementation of the indoor localization technology is the deployment of wireless devices. Among the above wireless technologies, WiFi-based technology has a wide range of commercial applications and requires only a small number of devices. For example, Linksys [5] sells mesh WiFi devices and provides a service called \"Linksys Aware\", which enables WiFi devices to monitor movement around the house and informs users when meaningful motion is detected, but it cannot provide the location of the target. Therefore, WiFi-based indoor localization technology has become a promising alternative technology for indoor localization [6]. WiFi-based indoor localization methods are divided into active indoor localization and passive indoor localization [7]. The active indoor localization methods require that the target is equipped with devices that can receive wireless signals and locate the target by identifying the location of the equipment [8]. Passive localization is also known as device-free localization (DFL), where the target does not need to wear any device and has a wide range of applications. Passive indoor localization is necessary in situations where signals are poor or cell phones are restricted, such as mines, but the location of the target may be still needed in these situations. WiFi-based DFL uses Received Signal Strength (RSS) or Channel State Information (CSI) as a localization indicator. RSS has the characteristics of simple collection and easy calculation, but the collected data itself have significant instability due to the multipath effect, NLOS effect, complex environment and different hardware designs [9]. RSS is not particularly sensitive to the perception of subtle movements, which may lead to the same localization results when the target moves in a small area [10]. Compared with RSS, CSI also suffers from problems such as multipath effects and an NLOS effect, but it includes the amplitude and phase information of multiple subcarriers, which can portray the channel characteristics better than RSS, effectively improving the resolution of multipath signals and further improving the accuracy of the indoor localization [11]. WiFi-based DFL includes two kinds of methods: fingerprint-based and model-based methods [12]. The model-based methods are based on physical laws combined with the actual situation to establish a physical model to estimate the location. This method has high localization accuracy, but it is difficult to adapt to complex environments. The fingerprint-based methods build a fingerprint database from different reference locations and then use machine learning or deep learning to establish a relationship from signal to location. This kind of method has high localization performance under the assumption that fingerprints corresponding to different locations are distinguishable. Therefore, many existing works focus on designing fingerprints. For example, [PERSON] et al. [13] designed a CSI tensor by reconstructing CSI time series through integrating WiFi transmission links, aiming to fully characterize the profiles of the locations of the target. [PERSON] et al. [14] spliced measured CSI over multiple WiFi bands to construct channel impulse response to achieve high localization accuracy. However, [PERSON] et al. [15] proves that when WiFi bandwidth is insufficient, signals located at different locations will be ambiguous, causing similarities in fingerprints corresponding to different locations.