Combining Fingerprinting and Proximity Positioning for Wi-Fi and BLE RSS Measurements

Received Signal Strength (RSS)-based positioning is widely used in smartphone applications due to its ease of deployment and low hardware requirements. Among available methods, fingerprinting provides high accuracy at the cost of increased computational complexity, while proximity-based approaches offer lower precision but are more computationally efficient. This paper presents a hybrid positioning method that combines both techniques by applying a proximity-based filtering process to reduce the search space in RSS fingerprinting systems using Bluetooth Low Energy (BLE) and Wi-Fi signals. The method operates in two stages: first, it filters out transmitters based on their RSS values; second, it excludes distant or incomplete reference fingerprints. Experimental results, using real-world measurements across various devices and environments, show that the proposed approach improves positioning accuracy by up to 15% and reduces computational time by over 20%, particularly when using BLE or combined BLE and Wi-Fi.