# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 1 Introduction  
Outdoor positioning technology has matured significantly, and is now widely used in both military and civilian applications, yielding substantial economic and social benefits [1]. Its accuracy and real-time performance have reached advanced levels. Satellite positioning, which primarily relies on line of sight in trilateration [2], is highly effective outdoors. However, a major unresolved challenge in both industry and academia is positioning, navigation, and timing (PNT) technology in environments where GNSS signals are interrupted [3]. Among these challenges, indoor positioning technology remains particularly difficult and has yet to achieve a fundamental breakthrough [4]. Current indoor positioning methods, such as infrared and ultrasonic positioning, face significant drawbacks--they are difficult to deploy, costly, and less accurate, often failing to achieve meter-level precision [5]. Wi-Fi indoor positioning technology has garnered significant attention due to its low cost, scalability, and versatility across various use scenarios. During the offline phase, a sampling grid is designed for the target area, dividing it into smaller segments [6]. At each intersection point of the grid, corresponding coordinates are assigned, and the Wi-Fi signal strength (RSSI) values within each small grid area are collected [7]. In the operational phase, the collected data are organized and constructed into a fingerprint library. When a new fingerprint is measured at an unknown location, fingerprint recognition methods typically estimate the position by identifying the center point of the most similar reference fingerprint [8]. The traditional method of constructing a fingerprint library involves manual, point-by-point data collection, which is labor-intensive and inefficient. While this approach may be feasible for small areas, it becomes prohibitively costly and time consuming in larger environments, such as nursing homes with numerous rooms and extensive spaces. For instance, in one study, a researcher spent 8 days collecting RSSI data at 83 preset sampling points across four rooms totaling 275 square meters. The data were gathered using three different devices to build the fingerprint library [9]. This method is extremely time consuming and resource intensive. To address this issue, two alternative approaches are typically considered: crowdsourcing and sparse collection. Crowdsourcing involves enlisting volunteers to collect data [10], a method that has gained popularity for fingerprint collection and updating [11]. However, crowdsourced data often suffer from issues related to the reliability of location labels and the uneven distribution of sample locations--common challenges in crowdsourced signal data [12]. Sparse collection, on the other hand, minimizes the data collection effort by reducing the number of sampling points, but this comes at the cost of less accurate environmental characterization [13]. Some researchers have also experimented with using interpolation to construct fingerprint libraries [14], but this approach struggles to ensure the accuracy of the interpolated data points. In this paper, we apply the model to various common nursing home apartment structures. The environmental feature perception model efficiently and conveniently explores the relationship between spatial parameters and signal strength. For spaces with similar architectural layouts, this approach eliminates the need for time-consuming and labor-intensive fingerprint collection. Instead, the model's training results can be leveraged to rapidly construct fingerprint libraries for specific areas. The environmental feature-aware model proposed in this paper minimizes manual intervention, demonstrates strong generalization capabilities, and facilitates the rapid construction and updating of fingerprint libraries by identifying and extracting complex features that influence RSSI signals. This adaptability to different environments and signal variations marks a significant advancement in indoor positioning technology.