Wi-Fi Channel Information-Based Positioning for Indoor Applications

Indoor positioning systems are essential for navigation, asset tracking, and smart building management. Traditional methods like Bluetooth, infrared, and UWB often require extra hardware and high costs, limiting scalability. This study presents a high-accuracy Wi-Fi-based positioning system using Channel State Information (CSI) and Received Signal Strength Indicator (RSSI). Since most devices already include Wi-Fi modules, the approach avoids additional infrastructure while enabling precise indoor localization. A mobile and Windows application collect CSI and RSSI data across 2.4 GHz and 5 GHz bands, and the system processes these signals using pattern matching, machine learning, and deep learning models to determine the most accurate localization method. The system supports use cases such as attendance tracking, event monitoring, and indoor navigation, with broader applications in healthcare, security, and smart buildings.