Indoor Positioning Algorithm Based on Signal Reception Strength

In this article, we explore RSSI-based indoor positioning using WiFi signals. RSSI (Received Signal Strength Indicator) serves as a metric for measuring the intensity of received wireless signals, which can be implemented in code through signal processing libraries that extract dBm values from network adapter data. WiFi, as a wireless local area network technology, utilizes radio waves to establish network connectivity between devices, enabling communication with the internet or other networks—typically managed via socket programming and network configuration APIs. The indoor environment (INDOOR) constitutes a critical factor in WiFi signal transmission, as structural obstructions and multipath propagation require algorithmic compensation through path loss models or fingerprinting techniques. Understanding RSSI WiFi indoor dynamics is therefore essential for optimizing signal transmission and reception, encompassing aspects such as signal strength calibration, radio wave propagation physics, and the impact of indoor environmental factors on wireless performance. Practical implementations often involve regression algorithms or machine learning models to map RSSI values to spatial coordinates, with key functions including signal filtering, distance estimation using log-normal shadowing models, and real-time location triangulation.