A large-scale RF-based Indoor Localization System Using Low-complexity Gaussian filter and improved Bayesian inference

Abstract

The growing convergence among mobile computing device and smart sensors boosts the development of ubiquitous computing and smart spaces, where localization is an essential part to realize the big vision. The general localization methods based on GPS and cellular techniques are not suitable for tracking numerous small size and limited power objects in the indoor case. In this paper, we propose and demonstrate a new localization method, this method is an easy-setup and cost-effective indoor localization system based on off-the-shelf active RFID technology. Our system is not only compatible with the future smart spaces and ubiquitous computing systems, but also suitable for large-scale indoor localization. The use of low-complexity Gaussian Filter (GF), Wheel Graph Model (WGM) and Probabilistic Localization Algorithm (PLA) make the proposed algorithm robust and suitable for large-scale indoor positioning from uncertainty, self-adjective to varying indoor environment. Using MATLAB simulation, we study the system performances, especially the dependence on a number of system and environment parameters, and their statistical properties. The simulation results prove that our proposed system is an accurate and cost-effective candidate for indoor localization.