A Sequential-Multi-Decision Scheme for WiFi Localization Using Vision-Based Refinement

Currently, most mobile devices have WIFI and camera modules to locate their position. However, there are two main challenges in large, highly similar indoor environments (localization accuracy and localization time). Aiming to balance these problems, we propose a sequential-multi-decision integrated system that combines WIFI and vision to acquire users' locations. This system has two phases: sequential fusion localization and adaptive multi-decision fusion localization. The former employs WIFI-based localization first, then image-based localization and fusion localization are used within the constraints of WIFI-based localization. In the WIFI-based localization phase, the gaussian process regression (GPR) model is used to construct a WIFI indoor map. Subsequently, we propose to apply the hybrid whale optimization algorithm (HWOA) to WIFI-based localization to improve its accuracy and stability. The latter uses an adaptive multi-decision fusion mechanism that integrates WIFI-based localization, image-based localization, and fusion localization to obtain the users' location finally. The experiments show the effectiveness of HWOA applied to WIFI-based localization. We also experimentally evaluate the proposed fusion algorithm with other state-of-the-art fusion algorithms (e.g., accuracy and time) in a real environment (an area larger than 10,000$m<^>{2}$m2). The experimental results show that the proposed fusion system is competitive.