# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 2 Related Work
### Fingerprint-Based DFL Method  
The fingerprint-based DFL methods generally include offline and online phases. In the offline phase, signals from different reference locations are first collected and fingerprints with differentiation are designed to build a fingerprint database. In the online phase, the location of the target is calculated by comparing the difference between the real-time collected values and the fingerprint database. The fingerprint-based methods are used to implement a mapping from signal to location by specific methods. [PERSON] et al. [21] firstly formulated a shared consistent representation using CSI measurements, employed the singular value decomposition (SVD) method to decompose the consistent representation to obtain distinguishable features, utilized t-distributed stochastic neighbor embedding (t-SNE) to capture hidden structural characteristics, and utilized the AdaBoost model to establish a robust relationship between the input and output for achieving indoor localization. [PERSON] et al. [22] firstly partitioned the entire environment into multiple subdomains using the K-means clustering method in order to implement large-scale DFL. Subsequently, they proceeded to train a corresponding number of DFL models for each subdomain, utilizing the Fresnel phase difference as the fingerprints. In this way, the large-scale DFL problem can be regarded as conventional DFL problems. In order to fully exploit the potential of the collected data set in DFL, [PERSON] et al. [23] proposed to employ a pixel-level multimodal representation of radio images to fuse both amplitude and phase features. They used a novel Conditional Generative Adversarial Network with Auxiliary Classifier (AC-GAN) model to generate artificial samples for further expanding the collected data set. Additionally, they employed a regression formulation to train the Convolutional Neural Networks (CNNs) for positioning. [PERSON] et al. [24] modeled the CSI change vector as a multivariate Gaussian distribution to analyze a human-induced variance of CSI measurements and used Bayesian filtering techniques to track the trajectory of an individual. [PERSON] et al. [25] proposed data rate (DR) fingerprinting, proposed a time-window mechanism with different fingerprint formulations, designed an access point (AP) switching strategy, and designed a new matching algorithm named dynamic nearest neighbors (DNNs) to achieve DFL. The above-mentioned works designed fingerprints manually, but it is difficult to address the fingerprint similarity problem by artificial fingerprints. [PERSON] et al. [26] demonstrate that the ratio-based features can reduce noise and increase the sensing range, so we transform the raw signal to a ratio-based fingerprint to illustrate the fingerprint similarity problem. In Figure 2, we compare the data of a period of time from one of thirty subcarriers corresponding to different locations. When only a small number of reference points are considered, i.e., three, the raw signals will have a certain degree of similarity, and the ratio-based fingerprints can tackle the fingerprint similarity problem well and distinguish the fingerprints corresponding to different locations, as shown in Figure 2a-c and Figure 2d-f, respectively. The red dots are reference points used for comparison, while the black dots are other reference points. But when there are too many reference points, e.g., six, the raw signals can be quite ambiguous, and the ratio-based features cannot distinguish well between the fingerprints corresponding to the different locations, as shown in Figure 2g-i. In the middle column and right column of Figure 2, the X-axis and Y-axis indicate the real and imaginary parts of the CSI signal, respectively. Figure 2: Results of the distinguishability of comparing the raw signal with the ratio-based features.