# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 1 Introduction  
The rapid advancement of wireless communication and mobile device technologies has enabled numerous applications, particularly Location-Based Services (LBS) [1]. While Global Navigation Satellite Systems (GNSS) and cellular networks provide accurate positioning in outdoor environments, they struggle in indoor or obstructed areas due to Non Line of Sight (NLOS) errors [2]. Consequently, there is a growing demand for Indoor Positioning Systems (IPS) that can meet varying requirements for accuracy and coverage depending on the specific indoor environment. To address these challenges and provide reliable indoor positioning solutions, researchers have extensively explored various technologies, as illustrated in Table 1. Among these, Radio Frequency (RF)-based positioning technologies have become particularly popular due to their unique advantages and adaptability. Within RF technologies, two main approaches are widely used: ranging-based techniques and proximity detection techniques. Ranging-based techniques [3] determine the precise distance between devices by measuring signal characteristics. Common methods in this category include Angle of Arrival (AoA) [4], Time of Arrival (ToA) [5], and Received Signal Strength Indicator(RSSI) [6]. These technologies hold a significant position in the field of indoor positioning because of their high precision and wide range of application scenarios. Among ranging-based techniques, AoA and ToA offer superior accuracy for indoor positioning systems. AoA can achieve centimeter-level precision and supports three-dimensional positioning, making it highly suitable for applications requiring fine-grained localization. ToA provides sub-meter accuracy with improved reliability and reduces the need for site-specific calibration, enhancing its practicality in various environments. However, both methods have practical limitations. Despite that some AoA methods like the I/Q method for full-hardware localization [7] may reduce costs and deployment workload, AoA still necessitates precise control of antenna arrays, introducing significant implementation challenges. Additionally, AoA algorithms usually rely on in-phase and quadrature data, leading to substantial resource consumption. ToA requires more expensive hardware, precise calibration, and ongoing maintenance. These factors make both methods less feasible for large-scale or cost-sensitive indoor positioning systems. In contrast, fingerprint-based indoor positioning, particularly Wi-Fi-based approaches, has emerged as a more accessible and cost-effective alternative. This method utilizes received Wi-Fi signals, such as the RSSI, from commonly available Access Points (APs) as unique location fingerprints. Unlike Global Positioning Systems (GPS), which struggles to penetrate indoor environments, Wi-Fi fingerprinting takes advantage of existing infrastructure without requiring additional hardware. Location fingerprints, including RSSI, Channel State Information (CSI), or geomagnetic field intensity, provide distinctive signatures for each location. The process typically consists of two stages: during the offline stage, RSSI measurements are collected at predefined Reference Points (RPs) and stored in a database. In the online stage, the user's position is estimated by comparing newly acquired RSSI values to the pre-recorded data [14; 15]. This approach, leveraging readily available Wi-Fi infrastructure, strikes a balance between accuracy, cost, and scalability, making it ideal for indoor positioning applications. \begin{table}
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One benefit of Wi-Fi RSSI fingerprinting is its affordability, as it leverages existing Wi-Fi networks without the need for costly sensors or additional infrastructure. CSI, while offering fine-grained localization, requires specialized hardware, making RSSI the more practical choice for widespread applications. However, RSSI is susceptible to multi-path effects and signal fading [16], impacting localization accuracy [17].