# Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment
## 4 The Proposed Method
### Problem Description  
In the time-varying environment, the accuracy of fingerprint-based DFL mainly depends on the quality of the fingerprint and the adaptability of the model. The distinguishability of fingerprints corresponding to different locations directly affects the accuracy of DFL. Since the bandwidth of WiFi cards is limited, for example, the Intel 5300 NIC has a bandwidth of up to 40 MHz, it directly limits the sensing ability of the WiFi signal, which leads to fingerprint similarity problem. At the same time, the multipath effect of the  
Figure 3: Main architecture of OS-ELM.  
indoor environment worsens this problem. We use the resonance strength to quantify the distinguishability between reference points (see Figure 4) [15].  
In Figure 4a, both the X-axis and Y-axis indicate reference points, and we use the CSI of 30 subcarriers between the first transmitting antenna and the first receiving antenna to calculate the resonance strength between different reference points. The signal with high similarity has a larger strength, up to 1. In this paper, we use two transmitting antennas and three receiving antennas, so there are six combinations, as shown in Figure 4a-f. From Figure 4, we can find that even with different antenna pairs, there is a certain degree of similarity between different reference points. Therefore, designing discriminable fingerprints is an important challenge.  
Additionally, during long-term indoor localization, the interaction between the target and the environment will have an impact on the environment and cause the environment to change. The environmental changes will affect the propagation of WiFi signals, causing the changes of fingerprints and affecting the accuracy of DFL. In the time-varying indoor environment, the WiFi signal is mainly affected by the following three factors:  
1. Due to the asynchronous clocks between transceivers, there exist random initial phase offsets caused by WiFi NIC initialization when the transceiver device is restarted.
2. The change of signal distribution when the transceiver is moved unintentionally.
3. Indoor multipath effect changes when the location of items in the indoor environment changes, which affects the signal propagation path.  
Figure 4: Resonance strength between reference points of different antenna pairs: (**a**) Signal between the first transmitting antenna and the first receiving antenna; (**b**) Signal between the first transmitting antenna and the second receiving antenna; (**c**) Signal between the first transmitting antenna and the third receiving antenna; (**d**) Signal between the second transmitting antenna and the first receiving antenna; (**e**) Signal between the second transmitting antenna and the second receiving antenna; (**f**) Signal between the second transmitting antenna and the third receiving antenna.  
We used data from one of the thirty subcarriers in the CSI over a period of time to compare the raw signal with the changed signal when the environment has changed, as shown in Figure 5, where the X-axis and Y-axis indicate the real and imaginary parts of the CSI signal, respectively. From Figure 5a, we can find that the signal has shifted when the device is powered off and restarted. But when the transceiver is moved by chance, the signal changes dramatically and will affect the fingerprints, which is shown in Figure 5b. Any change in the indoor environment will cause changes of the signal, which will affect the localization performance. Therefore, it is necessary to design an adaptive model and update it regularly. The adaptability of the model directly determines its ability to adapt to changes in the time-varying indoor environment.