A Sensor Fusion-Based Framework for Floor Localization

Abstract

Floor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro-electro-mechanical sensors (e.g., barometers) in handheld devices and wearable gadgets of current vintage has prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS) and barometric altimetry (BA) are two well-known methods of floor localization; however, RSS-based methods lack the required accuracy and BA-based methods are prone to random errors due to local changes in the air pressure, e.g., from approaching weather systems. Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this paper, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves >99% floor localization accuracy. The algorithm does not require new infrastructure and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.