HMF: Heatmap and WiFi Fingerprint-Based Indoor Localization with Building Layout Consideration

Abstract

In recent years, WiFi fingerprint-based localization has received much attention due to its deployment practicability. Although existing works show WiFi fingerprinting can achieve good localization accuracy, the experiments were conducted under their own testbeds within a small area and a short period. In this work, we investigate the impact of different indoor environmental factors, such as temporal and spatial similarity, on the performance of WiFi fingerprinting. We find that, WiFi fingerprinting is highly environment-sensitive. In an open space, it is quite challenging to find spatially varying but temporally stable signatures for adjacent reference locations. To address this issue, we propose a heatmap-based WiFi fingerprinting (called HMF) by utilizing layout construction as an additional input to improve WiFi fingerprint localization in open space environment. Our experimental results show, HMF can improve existing WiFi fingerprinting schemes like Radar and Horus by 28% and 80% in moderately open space, e.g., a wide corridor.