# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 2 Background and Related Works  
Wi-Fi fingerprinting is a wireless LAN-based indoor localization technique where a \"fingerprint\" refers to the collection of Wi-Fi signal strengths (RSSI) measured at a specific location. These fingerprints are stored in a database and serve as references for subsequent localization queries. Constructing an accurate Wi-Fi fingerprint library is crucial for the successful implementation of an indoor localization system. However, building such a database is time consuming [15], as it often involves generating location fingerprints by averaging the RSSI readings from a single location over an extended period [16]. Due to factors such as multipath effects and channel fading, the RSSI values received by the same device at the same location can vary significantly over consecutive time periods [17]. This inconsistency leads to discrepancies between the offline database and the RSSI values collected during the online phase. These discrepancies can greatly affect the algorithm's matching performance during localization, resulting in lower localization accuracy. [PERSON] et al. [18] proposed a moving window Wi-Fi location fingerprint acquisition and generation method, which addresses the issue of prolonged acquisition times in the traditional averaging method by introducing fixed-length and variable-length moving window strategies. While this approach effectively reduces acquisition time, it comes with a high degree of implementation complexity. The method requires setting the lengths of the fixed- and variable-length windows according to the specific environment, which involves intricate calculations and parameter adjustments. Additionally, it necessitates real-time data processing and continuous assessment of the signal's steady state during acquisition, demanding significant real-time processing capabilities from the system. In environments with frequent dynamic changes, the stability of the acquired fingerprint signal remains vulnerable, and can still be significantly affected. [PERSON] et al. [19] proposed a mobile fingerprinting method that replaces traditional static fingerprint collection at reference points by gathering real-time position information and RSSI data during walking. For this method to work, the data collected during movement must be highly synchronized with the actual position. Although the paper suggests using timestamps and high-precision handheld geo-alignment platforms to address this challenge, the effectiveness of these methods may be limited by various factors in real-world applications. This is particularly true during rapid movement or significant environmental changes, where the accuracy of data synchronization may still be an issue. Additionally, some scholars have explored constructing fingerprint libraries using methods based on the logarithmic distance path loss model [20], applying techniques such as Gaussian Process Regression (GPR) [21]. The environmental feature-aware model proposed in this paper effectively mitigates the impact of Wi-Fi signal fluctuations on localization accuracy. By capturing and recording environmental features, the model can anticipate changes in Wi-Fi signals, thereby enhancing the stability and consistency of fingerprint data. Furthermore, the model supports dynamic updates to the fingerprint database, allowing it to reflect real-time environmental changes. This dynamic adjustment ensures that the system maintains high localization accuracy across different environments. Additionally, leveraging advanced deep learning algorithms, the model can more accurately represent and predict environmental features and fingerprint data, improving both the accuracy and generalization capability of the localization system. In conclusion, the environmental feature-aware model demonstrates exceptional performance and broad adaptability in indoor localization, significantly enhancing the system's positioning accuracy and reliability.